
import pandas as pd
import numpy as np
import warnings
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, StackingRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.tree import plot_tree
from sklearn.model_selection import learning_curve


df = pd.read_csv("AAI Updated Dataset.csv")

df


print(list(df.columns))

['Country Name', 'Country Code', 'Region', 'IncomeGroup', '1990(E)', '1991(E)', '1992(E)', '1993(E)', '1994(E)', '1995(E)', '1996(E)', '1997(E)', '1998(E)', '1999(E)', '2000(E)', '2001(E)', '2002(E)', '2003(E)', '2004(E)', '2005(E)', '2006(E)', '2007(E)', '2008(E)', '2009(E)', '2010(E)', '2011(E)', '2012(E)', '2013(E)', '2014(E)', '2015(E)', '2016(E)', '2017(E)', '2018(E)', '2019(E)', '2020(E)', '2021(E)', '1960(EG)', '1961(EG)', '1962(EG)', '1963(EG)', '1964(EG)', '1965(EG)', '1966(EG)', '1967(EG)', '1968(EG)', '1969(EG)', '1970(EG)', '1971(EG)', '1972(EG)', '1973(EG)', '1974(EG)', '1975(EG)', '1976(EG)', '1977(EG)', '1978(EG)', '1979(EG)', '1980(EG)', '1981(EG)', '1982(EG)', '1983(EG)', '1984(EG)', '1985(EG)', '1986(EG)', '1987(EG)', '1988(EG)', '1989(EG)', '1990(EG)', '1991(EG)', '1992(EG)', '1993(EG)', '1994(EG)', '1995(EG)', '1996(EG)', '1997(EG)', '1998(EG)', '1999(EG)', '2000(EG)', '2001(EG)', '2002(EG)', '2003(EG)', '2004(EG)', '2005(EG)', '2006(EG)', '2007(EG)', '2008(EG)', '2009(EG)', '2010(EG)', '2011(EG)', '2012(EG)', '2013(EG)', '2014(EG)', '2015(EG)', '1990(CO2)', '1991(CO2)', '1992(CO2)', '1993(CO2)', '1994(CO2)', '1995(CO2)', '1996(CO2)', '1997(CO2)', '1998(CO2)', '1999(CO2)', '2000(CO2)', '2001(CO2)', '2002(CO2)', '2003(CO2)', '2004(CO2)', '2005(CO2)', '2006(CO2)', '2007(CO2)', '2008(CO2)', '2009(CO2)', '2010(CO2)', '2011(CO2)', '2012(CO2)', '2013(CO2)', '2014(CO2)', '2015(CO2)', '2016(CO2)', '2017(CO2)', '2018(CO2)', '2019(CO2)', '2020(CO2)', '1961(GDP annual growth)', '1962(GDP annual growth)', '1963(GDP annual growth)', '1964(GDP annual growth)', '1965(GDP annual growth)', '1966(GDP annual growth)', '1967(GDP annual growth)', '1968(GDP annual growth)', '1969(GDP annual growth)', '1970(GDP annual growth)', '1971(GDP annual growth)', '1972(GDP annual growth)', '1973(GDP annual growth)', '1974(GDP annual growth)', '1975(GDP annual growth)', '1976(GDP annual growth)', '1977(GDP annual growth)', '1978(GDP annual growth)', '1979(GDP annual growth)', '1980(GDP annual growth)', '1981(GDP annual growth)', '1982(GDP annual growth)', '1983(GDP annual growth)', '1984(GDP annual growth)', '1985(GDP annual growth)', '1986(GDP annual growth)', '1987(GDP annual growth)', '1988(GDP annual growth)', '1989(GDP annual growth)', '1990(GDP annual growth)', '1991(GDP annual growth)', '1992(GDP annual growth)', '1993(GDP annual growth)', '1994(GDP annual growth)', '1995(GDP annual growth)', '1996(GDP annual growth)', '1997(GDP annual growth)', '1998(GDP annual growth)', '1999(GDP annual growth)', '2000(GDP annual growth)', '2001(GDP annual growth)', '2002(GDP annual growth)', '2003(GDP annual growth)', '2004(GDP annual growth)', '2005(GDP annual growth)', '2006(GDP annual growth)', '2007(GDP annual growth)', '2008(GDP annual growth)', '2009(GDP annual growth)', '2010(GDP annual growth)', '2011(GDP annual growth)', '2012(GDP annual growth)', '2013(GDP annual growth)', '2014(GDP annual growth)', '2015(GDP annual growth)', '2016(GDP annual growth)', '2017(GDP annual growth)', '2018(GDP annual growth)', '2019(GDP annual growth)', '2020(GDP annual growth)', '2021(GDP annual growth)', '2022(GDP annual growth)', '1961(GDP per capita annual growth)', '1962(GDP per capita annual growth)', '1963(GDP per capita annual growth)', '1964(GDP per capita annual growth)', '1965(GDP per capita annual growth)', '1966(GDP per capita annual growth)', '1967(GDP per capita annual growth)', '1968(GDP per capita annual growth)', '1969(GDP per capita annual growth)', '1970(GDP per capita annual growth)', '1971(GDP per capita annual growth)', '1972(GDP per capita annual growth)', '1973(GDP per capita annual growth)', '1974(GDP per capita annual growth)', '1975(GDP per capita annual growth)', '1976(GDP per capita annual growth)', '1977(GDP per capita annual growth)', '1978(GDP per capita annual growth)', '1979(GDP per capita annual growth)', '1980(GDP per capita annual growth)', '1981(GDP per capita annual growth)', '1982(GDP per capita annual growth)', '1983(GDP per capita annual growth)', '1984(GDP per capita annual growth)', '1985(GDP per capita annual growth)', '1986(GDP per capita annual growth)', '1987(GDP per capita annual growth)', '1988(GDP per capita annual growth)', '1989(GDP per capita annual growth)', '1990(GDP per capita annual growth)', '1991(GDP per capita annual growth)', '1992(GDP per capita annual growth)', '1993(GDP per capita annual growth)', '1994(GDP per capita annual growth)', '1995(GDP per capita annual growth)', '1996(GDP per capita annual growth)', '1997(GDP per capita annual growth)', '1998(GDP per capita annual growth)', '1999(GDP per capita annual growth)', '2000(GDP per capita annual growth)', '2001(GDP per capita annual growth)', '2002(GDP per capita annual growth)', '2003(GDP per capita annual growth)', '2004(GDP per capita annual growth)', '2005(GDP per capita annual growth)', '2006(GDP per capita annual growth)', '2007(GDP per capita annual growth)', '2008(GDP per capita annual growth)', '2009(GDP per capita annual growth)', '2010(GDP per capita annual growth)', '2011(GDP per capita annual growth)', '2012(GDP per capita annual growth)', '2013(GDP per capita annual growth)', '2014(GDP per capita annual growth)', '2015(GDP per capita annual growth)', '2016(GDP per capita annual growth)', '2017(GDP per capita annual growth)', '2018(GDP per capita annual growth)', '2019(GDP per capita annual growth)', '2020(GDP per capita annual growth)', '2021(GDP per capita annual growth)', '2022(GDP per capita annual growth)', '1961(Population annual growth)', '1962(Population annual growth)', '1963(Population annual growth)', '1964(Population annual growth)', '1965(Population annual growth)', '1966(Population annual growth)', '1967(Population annual growth)', '1968(Population annual growth)', '1969(Population annual growth)', '1970(Population annual growth)', '1971(Population annual growth)', '1972(Population annual growth)', '1973(Population annual growth)', '1974(Population annual growth)', '1975(Population annual growth)', '1976(Population annual growth)', '1977(Population annual growth)', '1978(Population annual growth)', '1979(Population annual growth)', '1980(Population annual growth)', '1981(Population annual growth)', '1982(Population annual growth)', '1983(Population annual growth)', '1984(Population annual growth)', '1985(Population annual growth)', '1986(Population annual growth)', '1987(Population annual growth)', '1988(Population annual growth)', '1989(Population annual growth)', '1990(Population annual growth)', '1991(Population annual growth)', '1992(Population annual growth)', '1993(Population annual growth)', '1994(Population annual growth)', '1995(Population annual growth)', '1996(Population annual growth)', '1997(Population annual growth)', '1998(Population annual growth)', '1999(Population annual growth)', '2000(Population annual growth)', '2001(Population annual growth)', '2002(Population annual growth)', '2003(Population annual growth)', '2004(Population annual growth)', '2005(Population annual growth)', '2006(Population annual growth)', '2007(Population annual growth)', '2008(Population annual growth)', '2009(Population annual growth)', '2010(Population annual growth)', '2011(Population annual growth)', '2012(Population annual growth)', '2013(Population annual growth)', '2014(Population annual growth)', '2015(Population annual growth)', '2016(Population annual growth)', '2017(Population annual growth)', '2018(Population annual growth)', '2019(Population annual growth)', '2020(Population annual growth)', '2021(Population annual growth)', '2022(Population annual growth)']


df.head()


print("Dataset dimensions:", df.shape)

Dataset dimensions: (217, 309)


print("\nData types:\n", df.dtypes)

Data types:
 Country Name                       object
Country Code                       object
Region                             object
IncomeGroup                        object
1990(E)                           float64
                                   ...   
2018(Population annual growth)    float64
2019(Population annual growth)    float64
2020(Population annual growth)    float64
2021(Population annual growth)    float64
2022(Population annual growth)    float64
Length: 309, dtype: object


print("\nMissing values:\n", df.isnull().sum())

Missing values:
 Country Name                      0
Country Code                      0
Region                            0
IncomeGroup                       0
1990(E)                           0
                                 ..
2018(Population annual growth)    0
2019(Population annual growth)    0
2020(Population annual growth)    0
2021(Population annual growth)    0
2022(Population annual growth)    0
Length: 309, dtype: int64


df.describe().style.background_gradient(cmap="twilight_shifted")


# Extracting columns related to CO2 emissions from the DataFrame
co2_cols = [col for col in df.columns if 'CO2' in col]

# Extracting columns related to GDP per capita from the DataFrame
gdp_per_capita_cols = [col for col in df.columns if 'GDP per capita' in col]


print(co2_cols)
print(gdp_per_capita_cols)

['1990(CO2)', '1991(CO2)', '1992(CO2)', '1993(CO2)', '1994(CO2)', '1995(CO2)', '1996(CO2)', '1997(CO2)', '1998(CO2)', '1999(CO2)', '2000(CO2)', '2001(CO2)', '2002(CO2)', '2003(CO2)', '2004(CO2)', '2005(CO2)', '2006(CO2)', '2007(CO2)', '2008(CO2)', '2009(CO2)', '2010(CO2)', '2011(CO2)', '2012(CO2)', '2013(CO2)', '2014(CO2)', '2015(CO2)', '2016(CO2)', '2017(CO2)', '2018(CO2)', '2019(CO2)', '2020(CO2)']
['1961(GDP per capita annual growth)', '1962(GDP per capita annual growth)', '1963(GDP per capita annual growth)', '1964(GDP per capita annual growth)', '1965(GDP per capita annual growth)', '1966(GDP per capita annual growth)', '1967(GDP per capita annual growth)', '1968(GDP per capita annual growth)', '1969(GDP per capita annual growth)', '1970(GDP per capita annual growth)', '1971(GDP per capita annual growth)', '1972(GDP per capita annual growth)', '1973(GDP per capita annual growth)', '1974(GDP per capita annual growth)', '1975(GDP per capita annual growth)', '1976(GDP per capita annual growth)', '1977(GDP per capita annual growth)', '1978(GDP per capita annual growth)', '1979(GDP per capita annual growth)', '1980(GDP per capita annual growth)', '1981(GDP per capita annual growth)', '1982(GDP per capita annual growth)', '1983(GDP per capita annual growth)', '1984(GDP per capita annual growth)', '1985(GDP per capita annual growth)', '1986(GDP per capita annual growth)', '1987(GDP per capita annual growth)', '1988(GDP per capita annual growth)', '1989(GDP per capita annual growth)', '1990(GDP per capita annual growth)', '1991(GDP per capita annual growth)', '1992(GDP per capita annual growth)', '1993(GDP per capita annual growth)', '1994(GDP per capita annual growth)', '1995(GDP per capita annual growth)', '1996(GDP per capita annual growth)', '1997(GDP per capita annual growth)', '1998(GDP per capita annual growth)', '1999(GDP per capita annual growth)', '2000(GDP per capita annual growth)', '2001(GDP per capita annual growth)', '2002(GDP per capita annual growth)', '2003(GDP per capita annual growth)', '2004(GDP per capita annual growth)', '2005(GDP per capita annual growth)', '2006(GDP per capita annual growth)', '2007(GDP per capita annual growth)', '2008(GDP per capita annual growth)', '2009(GDP per capita annual growth)', '2010(GDP per capita annual growth)', '2011(GDP per capita annual growth)', '2012(GDP per capita annual growth)', '2013(GDP per capita annual growth)', '2014(GDP per capita annual growth)', '2015(GDP per capita annual growth)', '2016(GDP per capita annual growth)', '2017(GDP per capita annual growth)', '2018(GDP per capita annual growth)', '2019(GDP per capita annual growth)', '2020(GDP per capita annual growth)', '2021(GDP per capita annual growth)', '2022(GDP per capita annual growth)']


# Create a figure
fig = go.Figure()

# Add traces for each column in co2_cols
for col in co2_cols:
    fig.add_trace(go.Scatter(x=df.columns[4:], y=df[col], mode='lines', name=col))

# Update layout
fig.update_layout(
    title='CO2 Emissions Over Time',
    xaxis_title='Year',
    yaxis_title='CO2 emissions (kt)',
    hovermode='x',  # Display closest data point on hover
    height=600,     # Adjust height for better visibility
    width=1000      # Adjust width for better visibility
)

# Show plot
fig.show()


# Extracting the latest year from the column names
gdp_per_capita_columns = [col for col in df.columns if 'GDP per capita' in col]
latest_year = max(gdp_per_capita_columns, key=lambda x: int(x.split('(')[0]))

# Creating the 'latest_gdp_per_capita' column if it exists
if latest_year in df.columns:
    df['latest_gdp_per_capita'] = df[latest_year].apply(lambda x: round(x, 3))  # Round to two decimal places
else:
    print(f"The column {latest_year} does not exist.")

# Creating a bar plot using Plotly
fig = px.bar(df.nlargest(10, 'latest_gdp_per_capita'),
             x='Country Name',
             y='latest_gdp_per_capita',
             labels={'Country Name': 'Country', 'latest_gdp_per_capita': 'GDP per capita'},
             title=f'Top 10 Countries by GDP per Capita in {latest_year.split("(")[0]}',
             text='latest_gdp_per_capita')

# Customizing layout
fig.update_layout(xaxis_tickangle=-45,  # Rotate x-axis labels for better readability
                  xaxis_title='Country',
                  yaxis_title='GDP per capita',
                  height=600,  # Adjust height for better visibility
                  width=1000)  # Adjust width for better visibility

# Showing the plot
fig.show()


# Extracting the latest year from the column names
co2_columns = [col for col in df.columns if 'CO2' in col]
latest_year = max(co2_columns, key=lambda x: int(x.split('(')[0]))

# Creating the 'latest_co2' column if it exists
if latest_year in df.columns:
    df['latest_co2'] = df[latest_year].apply(lambda x: round(x, 2))  # Round to two decimal places
else:
    print(f"The column {latest_year} does not exist.")

# Creating a bar plot using Plotly
fig = px.bar(df.nlargest(10, 'latest_co2'),
             x='Country Name',
             y='latest_co2',
             labels={'Country Name': 'Country', 'latest_co2': 'CO2 Emissions'},
             title=f'Top 10 Countries by CO2 Emissions in {latest_year.split("(")[0]}',
             text='latest_co2')

# Customizing layout
fig.update_layout(xaxis_tickangle=-45,  # Rotate x-axis labels for better readability
                  xaxis_title='Country',
                  yaxis_title='CO2 Emissions',
                  height=600,  # Adjust height for better visibility
                  width=1000)  # Adjust width for better visibility

# Showing the plot
fig.show()


# Specify the latest year
latest_year = '2020'

# Create scatter plot using Plotly Express
fig = px.scatter(df, x=f'{latest_year}(GDP per capita annual growth)', y=f'{latest_year}(CO2)', 
                 labels={f'{latest_year}(GDP per capita annual growth)': f'GDP per capita annual growth in {latest_year}',
                         f'{latest_year}(CO2)': f'CO2 emissions in {latest_year}'},
                 title=f'ae{latest_year}')

# Customize layout
fig.update_layout(xaxis_title=f'GDP per capita annual growth in {latest_year}',
                  yaxis_title=f'CO2 emissions in {latest_year}',
                  height=600,
                  width=1000)

# Show the plot
fig.show()


# Assuming df is your DataFrame containing CO2 emissions and region information
# Group by region and sum CO2 emissions
df_region_co2 = df.groupby('Region')['2020(CO2)'].sum().reset_index()

# Get list of regions and corresponding CO2 emissions
regions = df_region_co2['Region']
co2_emissions = df_region_co2['2020(CO2)']

# Create a list to hold the data for each region
data = []

# Extract CO2 emissions for each region
for region in regions:
    region_co2 = df[df['Region'] == region]['2020(CO2)'].sum()
    data.append(region_co2)

# Create the stacked bar chart
fig = go.Figure(data=[go.Bar(x=regions, y=data)])

# Update layout
fig.update_layout(
    barmode='stack',
    title='CO2 Emissions by Region (2020)',
    xaxis_title='Region',
    yaxis_title='CO2 Emissions (metric tons)'
)

# Show the plot
fig.show()


# Sample DataFrame with hypothetical data
data = {
    'Country Name': ['Bangladesh', 'United States', 'Canada', 'Australia'],
    'Region': ['South Asia', 'North America', 'North America', 'East Asia & Pacific'],
    'IncomeGroup': ['Lower middle income', 'High income', 'High income', 'High income'],
    '2022(Population annual growth)': [2.0, 1.5, 0.9, 0.5]
}

# Selecting the columns for plotting
columns_to_plot = ['Country Name', 'Region', 'IncomeGroup']

# Plotting the area plot using Plotly
fig = go.Figure()

for index, row in df.iterrows():
    fig.add_trace(go.Scatter(x=columns_to_plot, y=row[columns_to_plot], mode='lines+markers', name=row['Country Name']))

fig.update_layout(
    title='Area Plot of Hypothetical Data',
    xaxis_title='Category',
    yaxis_title='Value',
    xaxis=dict(tickangle=-45),
    legend=dict(x=0, y=1.0),
    margin=dict(l=50, r=50, t=50, b=50),
    height=800,  # Increase the height
    width=1000    # Increase the width
)

fig.show()


# Suppress FutureWarning about iteritems
warnings.filterwarnings("ignore", category=FutureWarning)

# Assuming df is your DataFrame and features_list contains the selected features
selected_features = ['2015(E)', '2015(EG)', '2015(CO2)', '2015(GDP annual growth)', '2015(GDP per capita annual growth)', '2015(Population annual growth)']

# Select the subset of features from the DataFrame
df_selected = df[selected_features]

# Assign unique colors to each data point
num_samples = len(df_selected)
colors = np.linspace(0, 1, num_samples)

# Create the scatter plot matrix using Plotly Express
scatter_matrix = px.scatter_matrix(df_selected, color=colors, title='Scatter Plot Matrix of Selected Features (2020)', height=2000, width=2000)

# Show the plot
scatter_matrix.show()


feature1 = df['Country Name']
feature2 = df['Region']
feature3 = df['IncomeGroup']

# Define a colormap to map each region to a color
colormap = {
    'South Asia': 'rgb(255, 0, 0)',  # Red
    'North America': 'rgb(0, 0, 255)',  # Blue
    'East Asia & Pacific': 'rgb(0, 255, 0)',  # Green
    'Europe & Central Asia': 'rgb(255, 255, 0)',  # Yellow
    'Sub-Saharan Africa': 'rgb(255, 0, 255)',  # Magenta
    'Latin America & Caribbean': 'rgb(0, 255, 255)',  # Cyan
    'Middle East & North Africa': 'rgb(128, 0, 128)',  # Purple
    # Add more regions and colors as needed
}

# Create a list of colors corresponding to each region
colors = [colormap[region] for region in feature2]

# Create 3D scatter plot
fig = go.Figure(data=[go.Scatter3d(
    x=feature1,
    y=feature2,
    z=feature3,
    text=df['Country Name'],
    mode='markers',
    marker=dict(
        size=8,
        color=colors,  # Use the colors list
        opacity=0.8
    )
)])

# Update layout for better visualization
fig.update_layout(
    scene=dict(
        xaxis_title='Country Name',
        yaxis_title='Region',
        zaxis_title='IncomeGroup'
    ),
    title='3D Plot of Your Dataset',
    margin=dict(l=0, r=0, b=0, t=40),
    height=600,
    width=800
)

# Show interactive plot
fig.show()


years = range(1990, 2022)  

# Create a DataFrame containing only CO2 emissions data
co2_columns = [col for col in df.columns if 'CO2' in col]
co2_emissions_df = df[co2_columns]

# Set the index to a datetime index with yearly frequency
co2_emissions_df.index = pd.date_range(start=f'{years[0]}-01-01', periods=len(co2_emissions_df), freq='Y')

# Perform STL decomposition for each CO2 feature and create interactive plots
for column in co2_emissions_df.columns:
    decomposition = seasonal_decompose(co2_emissions_df[column], model='additive')
    
    # Create traces for each component
    traces = [
        go.Scatter(x=co2_emissions_df.index, y=co2_emissions_df[column], mode='lines', name='Original'),
        go.Scatter(x=co2_emissions_df.index, y=decomposition.trend, mode='lines', name='Trend'),
        go.Scatter(x=co2_emissions_df.index, y=decomposition.seasonal, mode='lines', name='Seasonal'),
        go.Scatter(x=co2_emissions_df.index, y=decomposition.resid, mode='lines', name='Residual')
    ]
    
    # Create layout for the plot
    layout = dict(title=f'STL Decomposition of {column}', xaxis=dict(title='Year'), yaxis=dict(title='CO2 Emissions'))
    
    # Create figure and plot
    fig = go.Figure(data=traces, layout=layout)
    fig.show()


# Filter columns to select only CO2 emission columns
co2_columns = [col for col in df.columns if 'CO2' in col]

# Create a new DataFrame with only the CO2 emission columns
df_co2 = df[co2_columns]

# If the CO2 emissions are in separate columns for each year, you can aggregate them
# For example, if you have columns like 'CO2_2018', 'CO2_2019', 'CO2_2020', etc.
# You can sum them up to get the total emissions for each year
df_co2_yearly = df_co2.sum(axis=0)

# Create a Plotly line plot for CO2 emissions over the years
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_co2_yearly.index, y=df_co2_yearly.values, mode='lines+markers', name='CO2 Emissions'))
fig.update_layout(title='CO2 Emissions Over the Years',
                  xaxis_title='Year',
                  yaxis_title='CO2 Emissions',
                  template='ggplot2')  # Set dark theme for the plot

# Show the plot
fig.show()


# Assuming df is your DataFrame and 'GDP per capita' is one of the features
gdp_per_capita_columns = [col for col in df.columns if 'GDP per capita' in col]
gdp_per_capita_df = df[gdp_per_capita_columns]

# Plot GDP per capita using Plotly Express
fig = px.line(gdp_per_capita_df, title='GDP per Capita Over Time')
fig.update_layout(xaxis_title='Year', yaxis_title='GDP per Capita')
fig.show()


# Reshape the DataFrame to have CO2 emissions year-wise for each country
co2_columns = [col for col in df.columns if 'CO2' in col]
co2_df = df[['Country Code', 'Country Name'] + co2_columns]
co2_df = co2_df.melt(id_vars=['Country Code', 'Country Name'], var_name='Year', value_name='CO2 emissions')
co2_df['Year'] = co2_df['Year'].str.extract('(\d+)').astype(int)

# Plot CO2 emissions on a world map
fig = px.choropleth(co2_df, 
                    locations='Country Code', 
                    color='CO2 emissions',
                    hover_name='Country Name',
                    animation_frame='Year',
                    color_continuous_scale='Viridis',  # You can change the color scale
                    title='CO2 Emissions Worldwide')
fig.update_layout(coloraxis_colorbar=dict(title='CO2 Emissions (metric tons)'))
fig.show()


# Select the features for analysis
features = [
    '2015(E)',
    '2015(EG)',
    '2015(CO2)',
    '2015(GDP annual growth)',
    '2015(GDP per capita annual growth)',
    '2015(Population annual growth)',
]

# Standardize the features
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df[features])

# Apply PCA for dimensionality reduction
pca = PCA(n_components=2)
pca_result = pca.fit_transform(scaled_features)

# Apply t-SNE for dimensionality reduction
tsne = TSNE(n_components=2, random_state=42)
tsne_result = tsne.fit_transform(scaled_features)

# Apply KMeans clustering
kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(scaled_features)
clusters = kmeans.predict(scaled_features)

# Create DataFrame for visualization
visualization_df = pd.DataFrame({
    'PCA1': pca_result[:, 0],
    'PCA2': pca_result[:, 1],
    't-SNE1': tsne_result[:, 0],
    't-SNE2': tsne_result[:, 1],
    'Cluster': clusters
})

# Plot interactive visualization
fig = px.scatter(visualization_df, x='PCA1', y='PCA2', color='Cluster', 
                 hover_data=[visualization_df.index], title='PCA Cluster Analysis')
fig.update_traces(marker=dict(size=8))
fig.show()

fig = px.scatter(visualization_df, x='t-SNE1', y='t-SNE2', color='Cluster', 
                 hover_data=[visualization_df.index], title='t-SNE Cluster Analysis')
fig.update_traces(marker=dict(size=8))
fig.show()


# Adjust the DataFrame to include only numeric columns
df_numeric = df.select_dtypes(include=['float64', 'int64'])

# Impute missing values with the mean
df_numeric = df_numeric.fillna(df_numeric.mean())

# Compute the correlation matrix for the numeric DataFrame
corr_matrix_numeric = df_numeric.corr()

# Create a heatmap figure using Plotly
fig = go.Figure(data=go.Heatmap(
                   z=corr_matrix_numeric.values,
                   x=corr_matrix_numeric.columns,
                   y=corr_matrix_numeric.columns,
                   colorscale='RdBu',
                   zmin=-1,
                   zmax=1,
                   colorbar=dict(
                       title='Correlation',
                       tickvals=[-1, -0.5, 0, 0.5, 1],
                       ticktext=['-1', '-0.5', '0', '0.5', '1'],
                       ticks='outside',
                       dtick=0.5
                   )
               ))

# Customize layout
fig.update_layout(
    title='Correlation Matrix',
    xaxis_title='Features',
    yaxis_title='Features',
    width=1000,
    height=1000
)

# Show the plot
fig.show()


columns_match = (corr_matrix_numeric.columns == df_numeric.columns).all()
index_match = (corr_matrix_numeric.index == df_numeric.columns).all()

print("\nColumns Match:", columns_match)
print("Index Match:", index_match)

Columns Match: True
Index Match: True


# Print the values of corr_matrix.columns and corr_matrix.index
print("\nColumns of Correlation Matrix:")
print(corr_matrix_numeric.columns)

Columns of Correlation Matrix:
Index(['1990(E)', '1991(E)', '1992(E)', '1993(E)', '1994(E)', '1995(E)',
       '1996(E)', '1997(E)', '1998(E)', '1999(E)',
       ...
       '2015(Population annual growth)', '2016(Population annual growth)',
       '2017(Population annual growth)', '2018(Population annual growth)',
       '2019(Population annual growth)', '2020(Population annual growth)',
       '2021(Population annual growth)', '2022(Population annual growth)',
       'latest_gdp_per_capita', 'latest_co2'],
      dtype='object', length=307)


print("\nIndex of Correlation Matrix:")
print(corr_matrix_numeric.index)

Index of Correlation Matrix:
Index(['1990(E)', '1991(E)', '1992(E)', '1993(E)', '1994(E)', '1995(E)',
       '1996(E)', '1997(E)', '1998(E)', '1999(E)',
       ...
       '2015(Population annual growth)', '2016(Population annual growth)',
       '2017(Population annual growth)', '2018(Population annual growth)',
       '2019(Population annual growth)', '2020(Population annual growth)',
       '2021(Population annual growth)', '2022(Population annual growth)',
       'latest_gdp_per_capita', 'latest_co2'],
      dtype='object', length=307)


# Create a boxplot using Plotly Graph Objects
fig = go.Figure()

# Add box trace for the '2015(E)' feature
feature = '2015(E)'
fig.add_trace(go.Box(
    y=df[feature],
    name=feature,
    boxmean=True,  # Show mean in the box
    boxpoints='outliers',  # Display outliers
    jitter=0.3,  # Spread out points for better visibility
    marker=dict(
        color='rgb(107,174,214)',  # Custom marker color
        outliercolor='rgba(219, 64, 82, 0.6)',  # Color for outliers
        line=dict(
            outliercolor='rgba(219, 64, 82, 0.6)',  # Outlier line color
            outlierwidth=2  # Outlier line width
        )
    ),
    hoverinfo='y'  # Show y-value on hover
))

# Customize layout
fig.update_layout(
    title='Boxplot of Selected Features',
    xaxis_title='Feature',
    yaxis_title='Value',
    showlegend=False,  # Hide legend initially
    height=1000,  # Set height of the plot (increased from 600)
    width=1000,  # Set width of the plot
)

# Add interactive features
fig.update_traces(
    hovertemplate='%{y:.2f}',  # Format hover text to two decimal places
    hoverlabel=dict(bgcolor='white', font_size=12),  # Style hover label
    boxmean=True  # Show mean in the box
)

# Show the plot
fig.show()


feature = '2015(E)'

# Define the outlier detection method
q1 = df[feature].quantile(0.25)
q3 = df[feature].quantile(0.75)
iqr = q3 - q1
lower_bound = (q1 - (1.5 * iqr))
upper_bound = (q3 + (1.5 * iqr))
outliers = df[(df[feature] < lower_bound) | (df[feature] > upper_bound)][feature]

# Create a box plot with outliers highlighted
fig = px.box(df, y=feature, title=f'Boxplot of {feature} with Outliers Highlighted', labels={'value': 'Value', 'variable': 'Feature'})
fig.update_traces(marker=dict(color='red'), boxmean=True)  # Customize marker color and display mean in the box
fig.add_scatter(x=outliers.index, y=outliers, mode='markers', marker=dict(color='red'), name='Outliers')  # Add outliers
fig.show()

# Print number of outliers
print(f"Number of outliers for {feature}: {outliers.shape[0]}")

Number of outliers for 2015(E): 23


# Create a boxplot using Plotly Graph Objects
fig = go.Figure()

# Add box trace for the '2015(EG)' feature
feature = '2015(EG)'
fig.add_trace(go.Box(
    y=df[feature],
    name=feature,
    boxmean=True,  # Show mean in the box
    boxpoints='outliers',  # Display outliers
    jitter=0.3,  # Spread out points for better visibility
    marker=dict(
        color='rgb(107,174,214)',  # Custom marker color
        outliercolor='rgba(219, 64, 82, 0.6)',  # Color for outliers
        line=dict(
            outliercolor='rgba(219, 64, 82, 0.6)',  # Outlier line color
            outlierwidth=2  # Outlier line width
        )
    ),
    hoverinfo='y'  # Show y-value on hover
))

# Customize layout
fig.update_layout(
    title='Boxplot of Selected Features',
    xaxis_title='Feature',
    yaxis_title='Value',
    showlegend=False,  # Hide legend initially
    height=1500,  # Set height of the plot (increased from 600)
    width=1000,  # Set width of the plot
)

# Add interactive features
fig.update_traces(
    hovertemplate='%{y:.2f}',  # Format hover text to two decimal places
    hoverlabel=dict(bgcolor='white', font_size=12),  # Style hover label
    boxmean=True  # Show mean in the box
)

# Show the plot
fig.show()


# Select the feature of interest
feature = '2015(EG)'

# Define the outlier detection method
q1 = df[feature].quantile(0.25)
q3 = df[feature].quantile(0.75)
iqr = q3 - q1
lower_bound = (q1 - (1.5 * iqr))
upper_bound = (q3 + (1.5 * iqr))
outliers = df[(df[feature] < lower_bound) | (df[feature] > upper_bound)][feature]

# Create a box plot with outliers highlighted
fig = px.box(df, y=feature, title=f'Boxplot of {feature} with Outliers Highlighted', labels={'value': 'Value', 'variable': 'Feature'})
fig.update_traces(marker=dict(color='red'), boxmean=True)  # Customize marker color and display mean in the box
fig.add_scatter(x=outliers.index, y=outliers, mode='markers', marker=dict(color='red'), name='Outliers')  # Add outliers
fig.show()

# Print number of outliers
print(f"Number of outliers for {feature}: {outliers.shape[0]}")

Number of outliers for 2015(EG): 34


# Create a boxplot using Plotly Graph Objects
fig = go.Figure()

# Add box trace for the '2015(CO2)' feature
feature = '2015(CO2)'
fig.add_trace(go.Box(
    y=df[feature],
    name=feature,
    boxmean=True,  # Show mean in the box
    boxpoints='outliers',  # Display outliers
    jitter=0.3,  # Spread out points for better visibility
    marker=dict(
        color='rgb(107,174,214)',  # Custom marker color
        outliercolor='rgba(219, 64, 82, 0.6)',  # Color for outliers
        line=dict(
            outliercolor='rgba(219, 64, 82, 0.6)',  # Outlier line color
            outlierwidth=2  # Outlier line width
        )
    ),
    hoverinfo='y'  # Show y-value on hover
))

# Customize layout
fig.update_layout(
    title='Boxplot of Selected Features',
    xaxis_title='Feature',
    yaxis_title='Value',
    showlegend=False,  # Hide legend initially
    height=1000,  # Set height of the plot (increased from 600)
    width=1000,  # Set width of the plot
)

# Add interactive features
fig.update_traces(
    hovertemplate='%{y:.2f}',  # Format hover text to two decimal places
    hoverlabel=dict(bgcolor='white', font_size=12),  # Style hover label
    boxmean=True  # Show mean in the box
)

# Show the plot
fig.show()


feature = '2015(CO2)'

# Define the outlier detection method
q1 = df[feature].quantile(0.25)
q3 = df[feature].quantile(0.75)
iqr = q3 - q1
lower_bound = (q1 - (1.5 * iqr))
upper_bound = (q3 + (1.5 * iqr))
outliers = df[(df[feature] < lower_bound) | (df[feature] > upper_bound)][feature]

# Create a box plot with outliers highlighted
fig = px.box(df, y=feature, title=f'Boxplot of {feature} with Outliers Highlighted', labels={'value': 'Value', 'variable': 'Feature'})
fig.update_traces(marker=dict(color='red'), boxmean=True)  # Customize marker color and display mean in the box
fig.add_scatter(x=outliers.index, y=outliers, mode='markers', marker=dict(color='red'), name='Outliers')  # Add outliers
fig.show()

# Print number of outliers
print(f"Number of outliers for {feature}: {outliers.shape[0]}")

Number of outliers for 2015(CO2): 34


# Define the list of features
best_features_for_boxplot = [
    '2015(GDP annual growth)',
    '2015(GDP per capita annual growth)',
    '2015(Population annual growth)'
]

# Create a boxplot using Plotly Graph Objects
fig = go.Figure()

# Add box traces for each feature
for feature in best_features_for_boxplot:
    fig.add_trace(go.Box(
        y=df[feature],
        name=feature,
        boxmean=True,  # Show mean in the box
        boxpoints='outliers',  # Display outliers
        jitter=0.3,  # Spread out points for better visibility
        marker=dict(
            color='rgb(107,174,214)',  # Custom marker color
            outliercolor='rgba(219, 64, 82, 0.6)',  # Color for outliers
            line=dict(
                outliercolor='rgba(219, 64, 82, 0.6)',  # Outlier line color
                outlierwidth=2  # Outlier line width
            )
        ),
        hoverinfo='y+name'  # Show y-value and feature name on hover
    ))

# Customize layout
fig.update_layout(
    title='Boxplot of Selected Features',
    xaxis_title='Feature',
    yaxis_title='Value',
    boxmode='group',  # Group box traces
    showlegend=False,  # Hide legend initially
    height=1000,  # Set height of the plot (increased from 600)
    width=1000  # Set width of the plot
)

# Add interactive features
fig.update_traces(
    hovertemplate='%{y:.2f}',  # Format hover text to two decimal places
    hoverlabel=dict(bgcolor='white', font_size=12),  # Style hover label
    boxmean=True  # Show mean in the box
)

# Show the plot
fig.show()


feature = '2015(GDP annual growth)'

# Define the outlier detection method
q1 = df[feature].quantile(0.25)
q3 = df[feature].quantile(0.75)
iqr = q3 - q1
lower_bound = (q1 - (1.5 * iqr))
upper_bound = (q3 + (1.5 * iqr))
outliers = df[(df[feature] < lower_bound) | (df[feature] > upper_bound)][feature]

# Create a box plot with outliers highlighted
fig = px.box(df, y=feature, title=f'Boxplot of {feature} with Outliers Highlighted', labels={'value': 'Value', 'variable': 'Feature'})
fig.update_traces(marker=dict(color='red'), boxmean=True)  # Customize marker color and display mean in the box
fig.add_scatter(x=outliers.index, y=outliers, mode='markers', marker=dict(color='red'), name='Outliers')  # Add outliers
fig.show()

# Print number of outliers
print(f"Number of outliers for {feature}: {outliers.shape[0]}")

Number of outliers for 2015(GDP annual growth): 14


feature = '2015(GDP per capita annual growth)'

# Define the outlier detection method
q1 = df[feature].quantile(0.25)
q3 = df[feature].quantile(0.75)
iqr = q3 - q1
lower_bound = (q1 - (1.5 * iqr))
upper_bound = (q3 + (1.5 * iqr))
outliers = df[(df[feature] < lower_bound) | (df[feature] > upper_bound)][feature]

# Create a box plot with outliers highlighted
fig = px.box(df, y=feature, title=f'Boxplot of {feature} with Outliers Highlighted', labels={'value': 'Value', 'variable': 'Feature'})
fig.update_traces(marker=dict(color='red'), boxmean=True)  # Customize marker color and display mean in the box
fig.add_scatter(x=outliers.index, y=outliers, mode='markers', marker=dict(color='red'), name='Outliers')  # Add outliers
fig.show()

# Print number of outliers
print(f"Number of outliers for {feature}: {outliers.shape[0]}")

Number of outliers for 2015(GDP per capita annual growth): 12


feature = '2015(Population annual growth)'

# Define the outlier detection method
q1 = df[feature].quantile(0.25)
q3 = df[feature].quantile(0.75)
iqr = q3 - q1
lower_bound = (q1 - (1.5 * iqr))
upper_bound = (q3 + (1.5 * iqr))
outliers = df[(df[feature] < lower_bound) | (df[feature] > upper_bound)][feature]

# Create a box plot with outliers highlighted
fig = px.box(df, y=feature, title=f'Boxplot of {feature} with Outliers Highlighted', labels={'value': 'Value', 'variable': 'Feature'})
fig.update_traces(marker=dict(color='red'), boxmean=True)  # Customize marker color and display mean in the box
fig.add_scatter(x=outliers.index, y=outliers, mode='markers', marker=dict(color='red'), name='Outliers')  # Add outliers
fig.show()

# Print number of outliers
print(f"Number of outliers for {feature}: {outliers.shape[0]}")

Number of outliers for 2015(Population annual growth): 3


# Define features for outlier removal by year
features_by_year = {
    '1990': ['1990(E)', '1990(EG)', '1990(CO2)', '1990(GDP annual growth)', '1990(GDP per capita annual growth)', '1990(Population annual growth)'],
    '1991': ['1991(E)', '1991(EG)', '1991(CO2)', '1991(GDP annual growth)', '1991(GDP per capita annual growth)', '1991(Population annual growth)'],
    '1992': ['1992(E)', '1992(EG)', '1992(CO2)', '1992(GDP annual growth)', '1992(GDP per capita annual growth)', '1992(Population annual growth)'],
    '1993': ['1993(E)', '1993(EG)', '1993(CO2)', '1993(GDP annual growth)', '1993(GDP per capita annual growth)', '1993(Population annual growth)'],
    '1994': ['1994(E)', '1994(EG)', '1994(CO2)', '1994(GDP annual growth)', '1994(GDP per capita annual growth)', '1994(Population annual growth)'],
    '1995': ['1995(E)', '1995(EG)', '1995(CO2)', '1995(GDP annual growth)', '1995(GDP per capita annual growth)', '1995(Population annual growth)'],
    '1996': ['1996(E)', '1996(EG)', '1996(CO2)', '1996(GDP annual growth)', '1996(GDP per capita annual growth)', '1996(Population annual growth)'],
    '1997': ['1997(E)', '1997(EG)', '1997(CO2)', '1997(GDP annual growth)', '1997(GDP per capita annual growth)', '1997(Population annual growth)'],
    '1998': ['1998(E)', '1998(EG)', '1998(CO2)', '1998(GDP annual growth)', '1998(GDP per capita annual growth)', '1998(Population annual growth)'],
    '1999': ['1999(E)', '1999(EG)', '1999(CO2)', '1999(GDP annual growth)', '1999(GDP per capita annual growth)', '1999(Population annual growth)'],
    '2000': ['2000(E)', '2000(EG)', '2000(CO2)', '2000(GDP annual growth)', '2000(GDP per capita annual growth)', '2000(Population annual growth)'],
    '2001': ['2001(E)', '2001(EG)', '2001(CO2)', '2001(GDP annual growth)', '2001(GDP per capita annual growth)', '2001(Population annual growth)'],
    '2002': ['2002(E)', '2002(EG)', '2002(CO2)', '2002(GDP annual growth)', '2002(GDP per capita annual growth)', '2002(Population annual growth)'],
    '2003': ['2003(E)', '2003(EG)', '2003(CO2)', '2003(GDP annual growth)', '2003(GDP per capita annual growth)', '2003(Population annual growth)'],
    '2004': ['2004(E)', '2004(EG)', '2004(CO2)', '2004(GDP annual growth)', '2004(GDP per capita annual growth)', '2004(Population annual growth)'],
    '2005': ['2005(E)', '2005(EG)', '2005(CO2)', '2005(GDP annual growth)', '2005(GDP per capita annual growth)', '2005(Population annual growth)'],
    '2006': ['2006(E)', '2006(EG)', '2006(CO2)', '2006(GDP annual growth)', '2006(GDP per capita annual growth)', '2006(Population annual growth)'],
    '2007': ['2007(E)', '2007(EG)', '2007(CO2)', '2007(GDP annual growth)', '2007(GDP per capita annual growth)', '2007(Population annual growth)'],
    '2008': ['2008(E)', '2008(EG)', '2008(CO2)', '2008(GDP annual growth)', '2008(GDP per capita annual growth)', '2008(Population annual growth)'],
    '2009': ['2009(E)', '2009(EG)', '2009(CO2)', '2009(GDP annual growth)', '2009(GDP per capita annual growth)', '2009(Population annual growth)'],
    '2010': ['2010(E)', '2010(EG)', '2010(CO2)', '2010(GDP annual growth)', '2010(GDP per capita annual growth)', '2010(Population annual growth)'],
    '2011': ['2011(E)', '2011(EG)', '2011(CO2)', '2011(GDP annual growth)', '2011(GDP per capita annual growth)', '2011(Population annual growth)'],
    '2012': ['2012(E)', '2012(EG)', '2012(CO2)', '2012(GDP annual growth)', '2012(GDP per capita annual growth)', '2012(Population annual growth)'],
    '2013': ['2013(E)', '2013(EG)', '2013(CO2)', '2013(GDP annual growth)', '2013(GDP per capita annual growth)', '2013(Population annual growth)'],
    '2014': ['2014(E)', '2014(EG)', '2014(CO2)', '2014(GDP annual growth)', '2014(GDP per capita annual growth)', '2014(Population annual growth)'],
    '2015': ['2015(E)', '2015(EG)', '2015(CO2)', '2015(GDP annual growth)', '2015(GDP per capita annual growth)', '2015(Population annual growth)'],
    '2016': ['2016(E)', '2016(CO2)', '2016(GDP annual growth)', '2016(GDP per capita annual growth)', '2016(Population annual growth)'],
    '2017': ['2017(E)', '2017(CO2)', '2017(GDP annual growth)', '2017(GDP per capita annual growth)', '2017(Population annual growth)'],
    '2018': ['2018(E)', '2018(CO2)', '2018(GDP annual growth)', '2018(GDP per capita annual growth)', '2018(Population annual growth)'],
    '2019': ['2019(E)', '2019(CO2)', '2019(GDP annual growth)', '2019(GDP per capita annual growth)', '2019(Population annual growth)'],
    '2020': ['2020(E)', '2020(CO2)', '2020(GDP annual growth)', '2020(GDP per capita annual growth)', '2020(Population annual growth)'],
    '2021': ['2021(E)', '2021(GDP annual growth)', '2021(GDP per capita annual growth)', '2021(Population annual growth)'],
    '2022': ['2022(GDP annual growth)', '2022(GDP per capita annual growth)', '2022(Population annual growth)'],
    '1960': ['1960(EG)'],
    '1961': ['1961(EG)', '1961(GDP annual growth)', '1961(GDP per capita annual growth)', '1961(Population annual growth)'],
    '1962': ['1962(EG)', '1962(GDP annual growth)', '1962(GDP per capita annual growth)', '1962(Population annual growth)'],
    '1963': ['1963(EG)', '1963(GDP annual growth)', '1963(GDP per capita annual growth)', '1963(Population annual growth)'],
    '1964': ['1964(EG)', '1964(GDP annual growth)', '1964(GDP per capita annual growth)', '1964(Population annual growth)'],
    '1965': ['1965(EG)', '1965(GDP annual growth)', '1965(GDP per capita annual growth)', '1965(Population annual growth)'],
    '1966': ['1966(EG)', '1966(GDP annual growth)', '1966(GDP per capita annual growth)', '1966(Population annual growth)'],
    '1967': ['1967(EG)', '1967(GDP annual growth)', '1967(GDP per capita annual growth)', '1967(Population annual growth)'],
    '1968': ['1968(EG)', '1968(GDP annual growth)', '1968(GDP per capita annual growth)', '1968(Population annual growth)'],
    '1969': ['1969(EG)', '1969(GDP annual growth)', '1969(GDP per capita annual growth)', '1969(Population annual growth)'],
    '1970': ['1970(EG)', '1970(GDP annual growth)', '1970(GDP per capita annual growth)', '1970(Population annual growth)'],
    '1971': ['1971(EG)', '1971(GDP annual growth)', '1971(GDP per capita annual growth)', '1971(Population annual growth)'],
    '1972': ['1972(EG)', '1972(GDP annual growth)', '1972(GDP per capita annual growth)', '1972(Population annual growth)'],
    '1973': ['1973(EG)', '1973(GDP annual growth)', '1973(GDP per capita annual growth)', '1973(Population annual growth)'],
    '1974': ['1974(EG)', '1974(GDP annual growth)', '1974(GDP per capita annual growth)', '1974(Population annual growth)'],
    '1975': ['1975(EG)', '1975(GDP annual growth)', '1975(GDP per capita annual growth)', '1975(Population annual growth)'],
    '1976': ['1976(EG)', '1976(GDP annual growth)', '1976(GDP per capita annual growth)', '1976(Population annual growth)'],
    '1977': ['1977(EG)', '1977(GDP annual growth)', '1977(GDP per capita annual growth)', '1977(Population annual growth)'],
    '1978': ['1978(EG)', '1978(GDP annual growth)', '1978(GDP per capita annual growth)', '1978(Population annual growth)'],
    '1979': ['1979(EG)', '1979(GDP annual growth)', '1979(GDP per capita annual growth)', '1979(Population annual growth)'],
    '1980': ['1980(EG)', '1980(GDP annual growth)', '1980(GDP per capita annual growth)', '1980(Population annual growth)'],
    '1981': ['1981(EG)', '1981(GDP annual growth)', '1981(GDP per capita annual growth)', '1981(Population annual growth)'],
    '1982': ['1982(EG)', '1982(GDP annual growth)', '1982(GDP per capita annual growth)', '1982(Population annual growth)'],
    '1983': ['1983(EG)', '1983(GDP annual growth)', '1983(GDP per capita annual growth)', '1983(Population annual growth)'],
    '1984': ['1984(EG)', '1984(GDP annual growth)', '1984(GDP per capita annual growth)', '1984(Population annual growth)'],
    '1985': ['1985(EG)', '1985(GDP annual growth)', '1985(GDP per capita annual growth)', '1985(Population annual growth)'],
    '1986': ['1986(EG)', '1986(GDP annual growth)', '1986(GDP per capita annual growth)', '1986(Population annual growth)'],
    '1987': ['1987(EG)', '1987(GDP annual growth)', '1987(GDP per capita annual growth)', '1987(Population annual growth)'],
    '1988': ['1988(EG)', '1988(GDP annual growth)', '1988(GDP per capita annual growth)', '1988(Population annual growth)'],
    '1989': ['1989(EG)', '1989(GDP annual growth)', '1989(GDP per capita annual growth)', '1989(Population annual growth)'],
}

# Create a copy of the original DataFrame
df_original = df.copy()

# Remove outliers using IQR method for each year
for year, features in features_by_year.items():
    for col in features:
        q1 = df[col].quantile(0.25)
        q3 = df[col].quantile(0.75)
        iqr = q3 - q1
        
        # Filter data within 1.5 IQR of quartiles
        df = df[(df[col] >= (q1 - 1.5 * iqr)) & (df[col] <= (q3 + 1.5 * iqr))]
    
# Reset index after filtering
df_filtered = df.reset_index(drop=True)

# Print informative message about outlier removal
print("After outlier removal, df has {} samples.".format(df_filtered.shape[0]))

# Print the new DataFrame
#print(df_filtered)

After outlier removal, df has 3 samples.


df_filtered


df_original


features = ['Country Name', 'Country Code', 'Region', 'IncomeGroup', '1990(E)', '1991(E)', '1992(E)', '1993(E)', '1994(E)', '1995(E)', '1996(E)', '1997(E)', '1998(E)', '1999(E)', '2000(E)', '2001(E)', '2002(E)', '2003(E)', '2004(E)', '2005(E)', '2006(E)', '2007(E)', '2008(E)', '2009(E)', '2010(E)', '2011(E)', '2012(E)', '2013(E)', '2014(E)', '2015(E)', '2016(E)', '2017(E)', '2018(E)', '2019(E)', '2020(E)', '2021(E)', '1960(EG)', '1961(EG)', '1962(EG)', '1963(EG)', '1964(EG)', '1965(EG)', '1966(EG)', '1967(EG)', '1968(EG)', '1969(EG)', '1970(EG)', '1971(EG)', '1972(EG)', '1973(EG)', '1974(EG)', '1975(EG)', '1976(EG)', '1977(EG)', '1978(EG)', '1979(EG)', '1980(EG)', '1981(EG)', '1982(EG)', '1983(EG)', '1984(EG)', '1985(EG)', '1986(EG)', '1987(EG)', '1988(EG)', '1989(EG)', '1990(EG)', '1991(EG)', '1992(EG)', '1993(EG)', '1994(EG)', '1995(EG)', '1996(EG)', '1997(EG)', '1998(EG)', '1999(EG)', '2000(EG)', '2001(EG)', '2002(EG)', '2003(EG)', '2004(EG)', '2005(EG)', '2006(EG)', '2007(EG)', '2008(EG)', '2009(EG)', '2010(EG)', '2011(EG)', '2012(EG)', '2013(EG)', '2014(EG)', '2015(EG)', '1990(CO2)', '1991(CO2)', '1992(CO2)', '1993(CO2)', '1994(CO2)', '1995(CO2)', '1996(CO2)', '1997(CO2)', '1998(CO2)', '1999(CO2)', '2000(CO2)', '2001(CO2)', '2002(CO2)', '2003(CO2)', '2004(CO2)', '2005(CO2)', '2006(CO2)', '2007(CO2)', '2008(CO2)', '2009(CO2)', '2010(CO2)', '2011(CO2)', '2012(CO2)', '2013(CO2)', '2014(CO2)', '2015(CO2)', '2016(CO2)', '2017(CO2)', '2018(CO2)', '2019(CO2)', '2020(CO2)', '1961(GDP annual growth)', '1962(GDP annual growth)', '1963(GDP annual growth)', '1964(GDP annual growth)', '1965(GDP annual growth)', '1966(GDP annual growth)', '1967(GDP annual growth)', '1968(GDP annual growth)', '1969(GDP annual growth)', '1970(GDP annual growth)', '1971(GDP annual growth)', '1972(GDP annual growth)', '1973(GDP annual growth)', '1974(GDP annual growth)', '1975(GDP annual growth)', '1976(GDP annual growth)', '1977(GDP annual growth)', '1978(GDP annual growth)', '1979(GDP annual growth)', '1980(GDP annual growth)', '1981(GDP annual growth)', '1982(GDP annual growth)', '1983(GDP annual growth)', '1984(GDP annual growth)', '1985(GDP annual growth)', '1986(GDP annual growth)', '1987(GDP annual growth)', '1988(GDP annual growth)', '1989(GDP annual growth)', '1990(GDP annual growth)', '1991(GDP annual growth)', '1992(GDP annual growth)', '1993(GDP annual growth)', '1994(GDP annual growth)', '1995(GDP annual growth)', '1996(GDP annual growth)', '1997(GDP annual growth)', '1998(GDP annual growth)', '1999(GDP annual growth)', '2000(GDP annual growth)', '2001(GDP annual growth)', '2002(GDP annual growth)', '2003(GDP annual growth)', '2004(GDP annual growth)', '2005(GDP annual growth)', '2006(GDP annual growth)', '2007(GDP annual growth)', '2008(GDP annual growth)', '2009(GDP annual growth)', '2010(GDP annual growth)', '2011(GDP annual growth)', '2012(GDP annual growth)', '2013(GDP annual growth)', '2014(GDP annual growth)', '2015(GDP annual growth)', '2016(GDP annual growth)', '2017(GDP annual growth)', '2018(GDP annual growth)', '2019(GDP annual growth)', '2020(GDP annual growth)', '2021(GDP annual growth)', '2022(GDP annual growth)', '1961(GDP per capita annual growth)', '1962(GDP per capita annual growth)', '1963(GDP per capita annual growth)', '1964(GDP per capita annual growth)', '1965(GDP per capita annual growth)', '1966(GDP per capita annual growth)', '1967(GDP per capita annual growth)', '1968(GDP per capita annual growth)', '1969(GDP per capita annual growth)', '1970(GDP per capita annual growth)', '1971(GDP per capita annual growth)', '1972(GDP per capita annual growth)', '1973(GDP per capita annual growth)', '1974(GDP per capita annual growth)', '1975(GDP per capita annual growth)', '1976(GDP per capita annual growth)', '1977(GDP per capita annual growth)', '1978(GDP per capita annual growth)', '1979(GDP per capita annual growth)', '1980(GDP per capita annual growth)', '1981(GDP per capita annual growth)', '1982(GDP per capita annual growth)', '1983(GDP per capita annual growth)', '1984(GDP per capita annual growth)', '1985(GDP per capita annual growth)', '1986(GDP per capita annual growth)', '1987(GDP per capita annual growth)', '1988(GDP per capita annual growth)', '1989(GDP per capita annual growth)', '1990(GDP per capita annual growth)', '1991(GDP per capita annual growth)', '1992(GDP per capita annual growth)', '1993(GDP per capita annual growth)', '1994(GDP per capita annual growth)', '1995(GDP per capita annual growth)', '1996(GDP per capita annual growth)', '1997(GDP per capita annual growth)', '1998(GDP per capita annual growth)', '1999(GDP per capita annual growth)', '2000(GDP per capita annual growth)', '2001(GDP per capita annual growth)', '2002(GDP per capita annual growth)', '2003(GDP per capita annual growth)', '2004(GDP per capita annual growth)', '2005(GDP per capita annual growth)', '2006(GDP per capita annual growth)', '2007(GDP per capita annual growth)', '2008(GDP per capita annual growth)', '2009(GDP per capita annual growth)', '2010(GDP per capita annual growth)', '2011(GDP per capita annual growth)', '2012(GDP per capita annual growth)', '2013(GDP per capita annual growth)', '2014(GDP per capita annual growth)', '2015(GDP per capita annual growth)', '2016(GDP per capita annual growth)', '2017(GDP per capita annual growth)', '2018(GDP per capita annual growth)', '2019(GDP per capita annual growth)', '2020(GDP per capita annual growth)', '2021(GDP per capita annual growth)', '2022(GDP per capita annual growth)', '1961(Population annual growth)', '1962(Population annual growth)', '1963(Population annual growth)', '1964(Population annual growth)', '1965(Population annual growth)', '1966(Population annual growth)', '1967(Population annual growth)', '1968(Population annual growth)', '1969(Population annual growth)', '1970(Population annual growth)', '1971(Population annual growth)', '1972(Population annual growth)', '1973(Population annual growth)', '1974(Population annual growth)', '1975(Population annual growth)', '1976(Population annual growth)', '1977(Population annual growth)', '1978(Population annual growth)', '1979(Population annual growth)', '1980(Population annual growth)', '1981(Population annual growth)', '1982(Population annual growth)', '1983(Population annual growth)', '1984(Population annual growth)', '1985(Population annual growth)', '1986(Population annual growth)', '1987(Population annual growth)', '1988(Population annual growth)', '1989(Population annual growth)', '1990(Population annual growth)', '1991(Population annual growth)', '1992(Population annual growth)', '1993(Population annual growth)', '1994(Population annual growth)', '1995(Population annual growth)', '1996(Population annual growth)', '1997(Population annual growth)', '1998(Population annual growth)', '1999(Population annual growth)', '2000(Population annual growth)', '2001(Population annual growth)', '2002(Population annual growth)', '2003(Population annual growth)', '2004(Population annual growth)', '2005(Population annual growth)', '2006(Population annual growth)', '2007(Population annual growth)', '2008(Population annual growth)', '2009(Population annual growth)', '2010(Population annual growth)', '2011(Population annual growth)', '2012(Population annual growth)', '2013(Population annual growth)', '2014(Population annual growth)', '2015(Population annual growth)', '2016(Population annual growth)', '2017(Population annual growth)', '2018(Population annual growth)', '2019(Population annual growth)', '2020(Population annual growth)', '2021(Population annual growth)', '2022(Population annual growth)', 'latest_gdp_per_capita']

target = 'latest_co2'


X = df_original[features]
y = df_original[target]


X = pd.get_dummies(X)


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)


# Define the hyperparameter grid
parameters = {'alpha': [0.1, 1, 10]}

# Instantiate the GridSearchCV object
ridge_grid = GridSearchCV(Ridge(), parameters)

# Fit the GridSearchCV object on the training data
ridge_grid.fit(X_train_scaled, y_train)

# Get the best alpha value from the grid search
best_alpha = ridge_grid.best_params_['alpha']
print(f"Best alpha for Ridge: {best_alpha}")

# Instantiate the Ridge Regression model with the best alpha
ridge = Ridge(alpha=best_alpha)

# Fit the Ridge model on the training data
ridge.fit(X_train_scaled, y_train)

# (Optional) Predict on the testing data using the Ridge model
y_pred_ridge = ridge.predict(X_test_scaled)

# Calculate evaluation metrics
mse_ridge = mean_squared_error(y_test, y_pred_ridge)
mae_ridge = mean_absolute_error(y_test, y_pred_ridge)
rmse_ridge = np.sqrt(mse_ridge)
r2_ridge = r2_score(y_test, y_pred_ridge)

# Print the evaluation metrics
print("Ridge Regression:")
print(f"- Mean Squared Error (Ridge): {mse_ridge}")
print(f"- Mean Absolute Error (Ridge): {mae_ridge}")
print(f"- Root Mean Squared Error (Ridge): {rmse_ridge}")
print(f"- R-squared (Ridge): {r2_ridge}")

Best alpha for Ridge: 0.1
Ridge Regression:
- Mean Squared Error (Ridge): 15576340795.818892
- Mean Absolute Error (Ridge): 81629.23186175199
- Root Mean Squared Error (Ridge): 124805.21141290091
- R-squared (Ridge): 0.43448503949473927


# Create a DataFrame for visualization
viz_data = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred_ridge})

# Scatter plot of actual vs. predicted values
fig = px.scatter(viz_data, x='Actual', y='Predicted', title='Actual vs. Predicted Values (Ridge Regression)')
fig.update_layout(xaxis_title='Actual', yaxis_title='Predicted')
fig.show()


# Calculate residuals
residuals = y_test - y_pred_ridge

# Scatter plot of residuals
fig = px.scatter(x=y_test, y=residuals, title='Residual Plot (Ridge Regression)')
fig.update_layout(xaxis_title='Actual', yaxis_title='Residuals', yaxis=dict(title='Residuals', zeroline=False))
fig.show()


# Histogram of residuals
fig = px.histogram(x=residuals, nbins=50, title='Distribution of Residuals (Ridge Regression)')
fig.update_layout(
    xaxis_title='Residuals', 
    yaxis_title='Frequency',
    margin=dict(t=50, b=50, l=50, r=50),  # Add a slight gap between the plot and the edges
    paper_bgcolor='rgba(255,255,255,0.9)',  # Set white background color
    plot_bgcolor='rgba(240,240,240,0.9)',  # Set white background color
    bargap=0.1  # Add a slight gap between the histograms
)
fig.show()


# Initialize Linear Regression
linear_regression = LinearRegression()

# Train the model
linear_regression.fit(X_train_scaled, y_train)

# Evaluate the model
y_pred = linear_regression.predict(X_test_scaled)
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)
r2 = r2_score(y_test, y_pred)

print("Linear Regression:")
print(f"- MSE: {mse}")
print(f"- MAE: {mae}")
print(f"- RMSE: {rmse}")
print(f"- R2: {r2}")

Linear Regression:
- MSE: 15319379246.472343
- MAE: 80348.82994515097
- RMSE: 123771.47993973548
- R2: 0.44381429097523406


# Create the scatter plot
fig = go.Figure()

# Add scatter plot for predicted vs actual values
fig.add_trace(go.Scatter(x=y_test, y=y_pred, mode='markers', marker=dict(color='blue'), name='Predicted vs Actual'))

# Add diagonal line (y=x) for reference
fig.add_trace(go.Scatter(x=y_test, y=y_test, mode='lines', line=dict(color='black', width=2, dash='dash'), name='y=x'))

# Update layout
fig.update_layout(
    title='Predicted vs Actual Plot',
    xaxis_title='Actual Values',
    yaxis_title='Predicted Values',
    showlegend=True,
    height=500,
    width=800
)

# Show plot
fig.show()


# Create the scatter plot
fig = go.Figure()

# Calculate residuals
residuals = y_test - y_pred

# Add scatter plot for residuals
fig.add_trace(go.Scatter(x=y_pred, y=residuals, mode='markers', marker=dict(color='blue'), name='Residuals'))

# Add horizontal line at y=0
fig.add_shape(type="line", x0=min(y_pred), y0=0, x1=max(y_pred), y1=0, line=dict(color="red", width=2, dash="dash"), name='Zero Residuals')

# Update layout
fig.update_layout(
    title='Residual Plot',
    xaxis_title='Predicted Values',
    yaxis_title='Residuals',
    showlegend=True,
    height=500,
    width=800
)

# Show plot
fig.show()


# Create the bar plot
fig = go.Figure()

# Extract feature importance from the model (replace 'model' with your trained linear regression model)
feature_importance = linear_regression.coef_

# Add bar plot for feature importances
fig.add_trace(go.Bar(x=feature_importance, y=X_train.columns, orientation='h', marker=dict(color='blue'), name='Feature Importance'))

# Update layout
fig.update_layout(
    title='Feature Importance Plot',
    xaxis_title='Feature Importance',
    yaxis_title='Feature',
    showlegend=False,
    height=2000,
    width=1000
)

# Show plot
fig.show()


# Initialize Decision Tree Regressor
decision_tree = DecisionTreeRegressor(random_state=42)

# Define hyperparameters for experimentation
param_grid = {
    'max_depth': [None, 5, 10, 15],  # Maximum depth of the tree
    'min_samples_split': [2, 5, 10],  # Minimum number of samples required to split an internal node
    'min_samples_leaf': [1, 2, 4],  # Minimum number of samples required to be at a leaf node
}

# Perform grid search with cross-validation
grid_search = GridSearchCV(estimator=decision_tree, param_grid=param_grid, 
                           cv=5, scoring='neg_mean_squared_error')
grid_search.fit(X_train_scaled, y_train)

# Get the best parameters
best_params = grid_search.best_params_

# Initialize Decision Tree Regressor with best parameters
best_decision_tree = DecisionTreeRegressor(random_state=42, **best_params)

# Train the model with best parameters
best_decision_tree.fit(X_train_scaled, y_train)

# Evaluate the model
y_pred = best_decision_tree.predict(X_test_scaled)
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)
r2 = r2_score(y_test, y_pred)

print("Decision Tree with Tuned Hyperparameters:")
print(f"- Best Parameters: {best_params}")
print(f"- MSE: {mse}")
print(f"- MAE: {mae}")
print(f"- RMSE: {rmse}")
print(f"- R2: {r2}")

Decision Tree with Tuned Hyperparameters:
- Best Parameters: {'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 5}
- MSE: 18647424274.05721
- MAE: 27348.99365530303
- RMSE: 136555.5721091498
- R2: 0.3229862173599225


def plot_prediction_vs_actual_decision_tree_interactive(model, X, y):
    y_pred = model.predict(X)
    
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=y, y=y_pred, mode='markers', 
                             marker=dict(color='blue', size=8, opacity=0.5),
                             name='Predictions'))
    fig.add_trace(go.Scatter(x=[y.min(), y.max()], y=[y.min(), y.max()], 
                             mode='lines', line=dict(color='black', width=2, dash='dash'),
                             name='Perfect Prediction'))
    
    fig.update_layout(title='Decision Tree Prediction vs. Actual Plot',
                      xaxis_title='Actual',
                      yaxis_title='Predicted',
                      hovermode='closest',
                      showlegend=True)
    
    fig.show()

# Assuming best_decision_tree, X_test_scaled, and y_test are defined
plot_prediction_vs_actual_decision_tree_interactive(best_decision_tree, X_test_scaled, y_test)


# Customize the plot style
plt.figure(figsize=(16, 10))  # Adjust the figure size
plot_tree(best_decision_tree, feature_names=X_train.columns.tolist(), filled=True,
          fontsize=10, precision=2, rounded=True, impurity=False,
          class_names=["Low", "Medium", "High"], max_depth=3)  # Enhance the tree visualization
plt.title("Decision Tree Visualization", fontsize=20)  # Set the title and adjust font size
plt.tight_layout()  # Improve spacing
plt.savefig("decision_tree_visualization.png")  # Save the plot as an image
plt.show()


# Extract feature importance from the model (replace 'model' with your trained decision tree model)
feature_importance_dt = best_decision_tree.feature_importances_

# Create a Plotly bar plot for feature importance
fig = go.Figure(data=[go.Bar(x=X_train.columns, y=feature_importance_dt, marker_color='green')])
fig.update_layout(title='Decision Tree Feature Importance Plot',
                  xaxis_title='Feature',
                  yaxis_title='Feature Importance',
                  width=2000, height=1200)
fig.show()


# Initialize Random Forest Regressor
random_forest = RandomForestRegressor(random_state=42)

# Define hyperparameters for experimentation
param_grid = {
    'n_estimators': [100, 200, 300],  # Number of trees in the forest
    'max_depth': [None, 5, 10, 20],  # Maximum depth of the tree
    'min_samples_split': [2, 5, 10],  # Minimum number of samples required to split an internal node
    'min_samples_leaf': [1, 2, 4],  # Minimum number of samples required to be at a leaf node
}

# Perform grid search with cross-validation
grid_search = GridSearchCV(estimator=random_forest, param_grid=param_grid, 
                           cv=5, scoring='neg_mean_squared_error')
grid_search.fit(X_train_scaled, y_train)

# Get the best parameters
best_params = grid_search.best_params_

# Initialize Random Forest Regressor with best parameters
best_random_forest = RandomForestRegressor(random_state=42, **best_params)

# Train the model with best parameters
best_random_forest.fit(X_train_scaled, y_train)

# Evaluate the model
y_pred = best_random_forest.predict(X_test_scaled)
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)
r2 = r2_score(y_test, y_pred)

print("Random Forest with Tuned Hyperparameters:")
print(f"- Best Parameters: {best_params}")
print(f"- MSE: {mse}")
print(f"- MAE: {mae}")
print(f"- RMSE: {rmse}")
print(f"- R2: {r2}")

Random Forest with Tuned Hyperparameters:
- Best Parameters: {'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}
- MSE: 9746868884.942074
- MAE: 26766.682104552576
- RMSE: 98726.2320001228
- R2: 0.6461299707825127


def plot_decision_tree(model, feature_names):
    fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(20, 10))
    plot_tree(model.estimators_[0], feature_names=feature_names.tolist(), filled=True, ax=axes, fontsize=10,
              impurity=False, precision=2, proportion=True, rounded=True, node_ids=True, 
              max_depth=5, label='root')
    plt.title("Decision Tree Visualization (Sample Tree)", fontsize=16)
    plt.show()

# Assuming you have already trained your random forest model (stored in 'best_random_forest') 
# and have feature names (stored in 'X_train.columns')
# You can replace 'best_random_forest' with your trained random forest model
plot_decision_tree(best_random_forest, X_train.columns)


def plot_mse_vs_trees_interactive(model):
    mse_values = [np.mean(tree.tree_.impurity[tree.apply(X_train)]) for tree in model.estimators_]
    
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=list(range(1, len(model.estimators_) + 1)), y=mse_values, mode='lines+markers', name='MSE'))
    fig.update_layout(title='Mean Squared Error (MSE) vs. Number of Trees',
                      xaxis_title='Number of Trees',
                      yaxis_title='Mean Squared Error (MSE)',
                      xaxis=dict(type='linear'),
                      yaxis=dict(type='linear'),
                      showlegend=True,
                      plot_bgcolor='rgba(0,0,0,0)')
    fig.show()

# Assuming you have already trained your random forest model and stored it in 'best_random_forest'
plot_mse_vs_trees_interactive(best_random_forest)

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but DecisionTreeRegressor was fitted without feature names


def plot_feature_importance(model, feature_names):
    feature_importance = model.feature_importances_
    feature_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importance})
    
    fig = px.bar(feature_df, x='Importance', y='Feature', orientation='h', 
                 title='Feature Importance in Random Forest Model',
                 labels={'Importance': 'Feature Importance', 'Feature': 'Features'},
                 color='Importance', color_continuous_scale='inferno')
    fig.update_layout(yaxis={'categoryorder':'total ascending'}, coloraxis_showscale=False)
    fig.show()

# Assuming you have already trained your random forest model (stored in 'best_random_forest') and have feature names
# You can replace 'best_random_forest' with your trained random forest model
plot_feature_importance(best_random_forest, X_train.columns.tolist())


# Initialize Gradient Boosting Regressor
gb_regressor = GradientBoostingRegressor(random_state=42)

# Define hyperparameters for grid search
param_grid = {
    'n_estimators': [100, 200, 300],  # Number of trees
    'learning_rate': [0.01, 0.1, 0.2],  # Learning rate
    'max_depth': [3, 5, 10],  # Maximum depth of trees
}

# Perform grid search with cross-validation
grid_search = GridSearchCV(estimator=gb_regressor, param_grid=param_grid, 
                           cv=5, scoring='neg_mean_squared_error')
grid_search.fit(X_train_scaled, y_train)

# Get the best parameters
best_params = grid_search.best_params_

# Initialize Gradient Boosting Regressor with best parameters
best_gb_regressor = GradientBoostingRegressor(random_state=42, **best_params)

# Train the model with best parameters
best_gb_regressor.fit(X_train_scaled, y_train)

# Evaluate the model
y_pred = best_gb_regressor.predict(X_test_scaled)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Gradient Boosting with Tuned Hyperparameters:")
print(f"- Best Parameters: {best_params}")
print(f"- MAE: {mae}")
print(f"- RMSE: {rmse}")
print(f"- MSE: {mse}")
print(f"- R2: {r2}")

Gradient Boosting with Tuned Hyperparameters:
- Best Parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}
- MAE: 26766.682104552576
- RMSE: 98726.2320001228
- MSE: 11475343277.93204
- R2: 0.5833759426767295


def plot_prediction_vs_actual_interactive(y_test, y_pred):
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=y_test, y=y_pred, mode='markers', 
                             marker=dict(color='blue', opacity=0.5), 
                             name='Predicted vs Actual'))
    fig.add_trace(go.Scatter(x=y_test, y=y_test, mode='lines', 
                             line=dict(color='black', dash='dash'), 
                             name='Perfect Prediction'))
    fig.update_layout(title='Prediction vs. Actual Plot',
                      xaxis_title='Actual',
                      yaxis_title='Predicted',
                      hovermode='closest')
    fig.show()

# Assuming y_test and y_pred are defined
plot_prediction_vs_actual_interactive(y_test, y_pred)


def plot_residuals_interactive(y_test, y_pred):
    residuals = y_test - y_pred
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=y_pred, y=residuals, mode='markers', 
                             marker=dict(color='blue', opacity=0.5), 
                             name='Residuals'))
    fig.add_hline(y=0, line_dash='dash', line_color='red', 
                  annotation_text='Zero Residual Line', 
                  annotation_position='bottom right')
    fig.update_layout(title='Residual Plot',
                      xaxis_title='Predicted',
                      yaxis_title='Residuals',
                      hovermode='closest')
    fig.show()

# Assuming y_test and y_pred are defined
plot_residuals_interactive(y_test, y_pred)


def plot_learning_curve_interactive(model, X, y):
    train_sizes, train_scores, test_scores = learning_curve(model, X, y, cv=5, scoring='neg_mean_squared_error')
    train_scores_mean = np.mean(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    
    fig = go.Figure()
    # Plot training scores
    fig.add_trace(go.Scatter(x=train_sizes, y=train_scores_mean, mode='lines', name='Training Score',
                             line=dict(color='red')))
    # Plot cross-validation scores
    fig.add_trace(go.Scatter(x=train_sizes, y=test_scores_mean, mode='lines', name='Cross-validation Score',
                             line=dict(color='green')))
    # Add shaded regions representing the standard deviation of scores
    fig.add_trace(go.Scatter(x=train_sizes, y=train_scores_mean + np.std(train_scores, axis=1),
                             mode='lines', fill='tonexty', fillcolor='rgba(255,0,0,0.3)', showlegend=False))
    fig.add_trace(go.Scatter(x=train_sizes, y=train_scores_mean - np.std(train_scores, axis=1),
                             mode='lines', fill='tonexty', fillcolor='rgba(255,0,0,0.3)', showlegend=False))
    fig.add_trace(go.Scatter(x=train_sizes, y=test_scores_mean + np.std(test_scores, axis=1),
                             mode='lines', fill='tonexty', fillcolor='rgba(0,255,0,0.3)', showlegend=False))
    fig.add_trace(go.Scatter(x=train_sizes, y=test_scores_mean - np.std(test_scores, axis=1),
                             mode='lines', fill='tonexty', fillcolor='rgba(0,255,0,0.3)', showlegend=False))
    
    # Update layout
    fig.update_layout(title='Learning Curve',
                      xaxis_title='Training Examples',
                      yaxis_title='Score',
                      hovermode='closest')
    fig.show()

# Assuming X_train_scaled and y_train are defined
plot_learning_curve_interactive(best_gb_regressor, X_train_scaled, y_train)


def plot_learning_rate_curve_interactive(X_train, y_train, X_test, y_test):
    learning_rates = np.linspace(0.01, 1.0, 10)
    train_scores = []
    test_scores = []
    for learning_rate in learning_rates:
        gb_regressor = GradientBoostingRegressor(learning_rate=learning_rate, random_state=42)
        gb_regressor.fit(X_train, y_train)
        train_scores.append(gb_regressor.score(X_train, y_train))
        test_scores.append(gb_regressor.score(X_test, y_test))

    fig = go.Figure()
    # Plot training scores
    fig.add_trace(go.Scatter(x=learning_rates, y=train_scores, mode='lines+markers', name='Training Score',
                             line=dict(color='red')))
    # Plot test scores
    fig.add_trace(go.Scatter(x=learning_rates, y=test_scores, mode='lines+markers', name='Test Score',
                             line=dict(color='green')))
    
    # Update layout
    fig.update_layout(title='Learning Rate Curve',
                      xaxis_title='Learning Rate',
                      yaxis_title='Score',
                      hovermode='closest')
    fig.show()

# Assuming X_train_scaled, X_test_scaled, y_train, and y_test are defined
plot_learning_rate_curve_interactive(X_train_scaled, y_train, X_test_scaled, y_test)


# Initialize MLP Regressor with adjusted parameters
mlp_regressor = MLPRegressor(
    hidden_layer_sizes=(200, 100),  # Adjusted hidden layer sizes for better generalization
    activation='relu',
    alpha=0.0001,  # Decreased alpha for regularization
    learning_rate_init=0.01,  # Decreased learning rate for smoother convergence
    max_iter=5000,  # Increased max_iter for better convergence
    early_stopping=True,
    validation_fraction=0.2
)

# Train the model
history = mlp_regressor.fit(X_train_scaled, y_train)

# Predict on the test set
y_pred = mlp_regressor.predict(X_test_scaled)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)
r2 = r2_score(y_test, y_pred)

print("Neural Network with Tuned Hyperparameters:")
print(f"MSE: {mse}")
print(f"MAE: {mae}")
print(f"RMSE: {rmse}")
print(f"R2: {r2}")

Neural Network with Tuned Hyperparameters:
MSE: 15942561347.07892
MAE: 90547.34294127848
RMSE: 126263.85605975655
R2: 0.4211890283649865


def plot_prediction_vs_actual_interactive(model, X, y):
    y_pred = model.predict(X)
    pred_actual_df = pd.DataFrame({'Actual': y, 'Predicted': y_pred})
    
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=pred_actual_df['Actual'], y=pred_actual_df['Predicted'], 
                             mode='markers', marker=dict(color='blue'), 
                             name='Predicted vs Actual'))
    
    # Corrected line to represent y=x
    fig.add_trace(go.Scatter(x=pred_actual_df['Actual'], y=pred_actual_df['Actual'], 
                             mode='lines', line=dict(color='black', width=2, dash='dash'), 
                             name='y=x'))
    
    fig.update_layout(title='Prediction vs. Actual Plot',
                      xaxis_title='Actual Values',
                      yaxis_title='Predicted Values',
                      showlegend=True,
                      height=500,
                      width=800)
    
    fig.show()

# Assuming you have already trained your neural network model (stored in 'mlp_regressor') 
# and have test data (stored in 'X_test' and 'y_test')
plot_prediction_vs_actual_interactive(mlp_regressor, X_test, y_test)

/Users/abdullahalrakin/anaconda3/lib/python3.11/site-packages/sklearn/base.py:457: UserWarning:

X has feature names, but MLPRegressor was fitted without feature names


def plot_loss_curve_interactive(history):
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=list(range(1, len(history.loss_curve_) + 1)), 
                             y=history.loss_curve_, mode='lines', name='Training Loss',
                             line=dict(color='blue')))
    fig.add_trace(go.Scatter(x=list(range(1, len(history.validation_scores_) + 1)), 
                             y=history.validation_scores_, mode='lines', name='Validation Loss',
                             line=dict(color='orange')))
    fig.update_layout(title='Loss Curve (Training vs. Validation)',
                      xaxis_title='Epochs',
                      yaxis_title='Loss',
                      hovermode='closest')
    fig.show()

# Plot loss curve interactively
plot_loss_curve_interactive(history)


def plot_learning_curve_interactive(estimator, X, y, ylim=None, cv=None,
                                    n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
    
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    
    fig = go.Figure()

    # Plot training scores
    fig.add_trace(go.Scatter(x=train_sizes, y=train_scores_mean - train_scores_std,
                             mode='lines', name='Training Score (Mean - Std)',
                             line=dict(color='rgba(255, 165, 0, 0.5)')))
    fig.add_trace(go.Scatter(x=train_sizes, y=train_scores_mean + train_scores_std,
                             mode='lines', name='Training Score (Mean + Std)',
                             line=dict(color='rgba(255, 165, 0, 0.5)'),
                             fill='tonexty'))

    # Plot validation scores
    fig.add_trace(go.Scatter(x=train_sizes, y=test_scores_mean - test_scores_std,
                             mode='lines', name='Cross-validation Score (Mean - Std)',
                             line=dict(color='rgba(0, 128, 128, 0.5)')))
    fig.add_trace(go.Scatter(x=train_sizes, y=test_scores_mean + test_scores_std,
                             mode='lines', name='Cross-validation Score (Mean + Std)',
                             line=dict(color='rgba(0, 128, 128, 0.5)'),
                             fill='tonexty'))

    # Plot means
    fig.add_trace(go.Scatter(x=train_sizes, y=train_scores_mean,
                             mode='lines+markers', name='Training Score Mean',
                             line=dict(color='rgb(255, 165, 0)')))
    fig.add_trace(go.Scatter(x=train_sizes, y=test_scores_mean,
                             mode='lines+markers', name='Cross-validation Score Mean',
                             line=dict(color='rgb(0, 128, 128)')))
    
    # Add layout
    fig.update_layout(title='Learning Curve',
                      xaxis_title='Training Examples',
                      yaxis_title='Score',
                      hovermode='closest')
    
    fig.show()

    # Assuming you have already trained your MLPRegressor model and have your data stored in X_train_scaled and y_train
plot_learning_curve_interactive(mlp_regressor, X_train_scaled, y_train)


# Define models and their corresponding performance metrics
models = ['Ridge Regression', 'Linear Regression', 'Decision Tree', 'Random Forest', 'Gradient Boosting', 'Neural Network']
mse_scores = [15576340795.818892, 15319379246.472343, 18647424274.05721, 9746868884.942074, 11475343277.93204, 15942561347.07892]
mae_scores = [81629.23186175199, 80348.82994515097, 27348.99365530303, 26766.682104552576, 26766.682104552576, 90547.34294127848]
rmse_scores = [124805.21141290091, 123771.47993973548, 136555.5721091498, 98726.2320001228, 98726.2320001228, 126263.85605975655]
r2_scores = [0.43448503949473927, 0.44381429097523406, 0.3229862173599225, 0.6461299707825127, 0.5833759426767295, 0.4211890283649865]

# Create traces for each performance metric
mse_trace = go.Bar(x=models, y=mse_scores, name='MSE', marker=dict(color='blue'))
mae_trace = go.Bar(x=models, y=mae_scores, name='MAE', marker=dict(color='orange'))
rmse_trace = go.Bar(x=models, y=rmse_scores, name='RMSE', marker=dict(color='green'))
r2_trace = go.Bar(x=models, y=r2_scores, name='R2', marker=dict(color='red'))

# Create subplots
fig = go.Figure(data=[mse_trace, mae_trace, rmse_trace, r2_trace])

# Update layout for better presentation
fig.update_layout(
    title='Comparison of Performance Metrics Across Models',
    xaxis=dict(title='Models', tickangle=-45),
    yaxis=dict(title='Scores'),
    barmode='group',
    legend=dict(x=1, y=1),
    plot_bgcolor='rgba(0,0,0,0)'
)

# Show interactive plot
fig.show()

	Country Name	Country Code	Region	IncomeGroup	1990(E)	1991(E)	1992(E)	1993(E)	1994(E)	1995(E)	...	2013(Population annual growth)	2014(Population annual growth)	2015(Population annual growth)	2016(Population annual growth)	2017(Population annual growth)	2018(Population annual growth)	2019(Population annual growth)	2020(Population annual growth)	2021(Population annual growth)	2022(Population annual growth)
0	Aruba	ABW	Latin America & Caribbean	High income	100.0	99.153656	99.197128	99.239914	100.000000	100.000000	...	0.749301	0.691615	0.637959	0.590062	0.537296	0.494795	0.451970	0.134255	-0.045045	-0.086392
1	Afghanistan	AFG	South Asia	Low income	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	3.466788	3.657576	3.121341	2.581549	2.866492	2.885208	2.908529	3.134747	2.851358	2.534498
2	Angola	AGO	Sub-Saharan Africa	Lower middle income	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	3.735525	3.684429	3.617678	3.586211	3.550987	3.464457	3.395278	3.268348	3.166030	3.096753
3	Albania	ALB	Europe & Central Asia	Upper middle income	100.0	100.000000	100.000000	100.000000	100.000000	100.000000	...	-0.183211	-0.207047	-0.291206	-0.159880	-0.091972	-0.246732	-0.426007	-0.574207	-0.926918	-1.215790
4	Andorra	AND	Europe & Central Asia	High income	100.0	100.000000	100.000000	100.000000	100.000000	100.000000	...	0.497262	0.355275	0.174378	1.100603	1.772183	1.580147	1.757491	1.761891	1.702288	0.994607
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
212	Kosovo	XKX	Europe & Central Asia	Upper middle income	100.0	100.000000	100.000000	100.000000	100.000000	100.000000	...	0.607468	-0.294474	-1.364932	-0.596734	0.753585	0.339011	-0.457730	0.070131	-0.229016	-1.355874
213	Yemen, Rep.	YEM	Middle East & North Africa	Low income	0.0	0.000000	44.100000	40.774715	41.985134	43.193512	...	2.859237	2.811072	2.712955	2.621537	2.564321	2.486360	2.426208	2.310447	2.137790	2.144628
214	South Africa	ZAF	Sub-Saharan Africa	Upper middle income	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	1.361621	1.576294	2.074017	0.972004	0.387278	1.225530	1.295074	1.223179	0.998920	0.841058
215	Zambia	ZMB	Sub-Saharan Africa	Lower middle income	13.9	12.752703	19.200000	14.213207	14.941759	15.668272	...	3.271299	3.247118	3.191896	3.147407	3.113595	3.061888	3.007618	2.933818	2.840806	2.758032
216	Zimbabwe	ZWE	Sub-Saharan Africa	Lower middle income	0.0	0.000000	28.200000	31.163668	28.100000	31.948580	...	2.163267	2.191391	2.136294	2.081806	2.043620	2.020537	1.989253	2.031112	2.045715	2.024036

	Country Name	Country Code	Region	IncomeGroup	1990(E)	1991(E)	1992(E)	1993(E)	1994(E)	1995(E)	...	2013(Population annual growth)	2014(Population annual growth)	2015(Population annual growth)	2016(Population annual growth)	2017(Population annual growth)	2018(Population annual growth)	2019(Population annual growth)	2020(Population annual growth)	2021(Population annual growth)	2022(Population annual growth)
0	Aruba	ABW	Latin America & Caribbean	High income	100.0	99.153656	99.197128	99.239914	100.0	100.0	...	0.749301	0.691615	0.637959	0.590062	0.537296	0.494795	0.451970	0.134255	-0.045045	-0.086392
1	Afghanistan	AFG	South Asia	Low income	0.0	0.000000	0.000000	0.000000	0.0	0.0	...	3.466788	3.657576	3.121341	2.581549	2.866492	2.885208	2.908529	3.134747	2.851358	2.534498
2	Angola	AGO	Sub-Saharan Africa	Lower middle income	0.0	0.000000	0.000000	0.000000	0.0	0.0	...	3.735525	3.684429	3.617678	3.586211	3.550987	3.464457	3.395278	3.268348	3.166030	3.096753
3	Albania	ALB	Europe & Central Asia	Upper middle income	100.0	100.000000	100.000000	100.000000	100.0	100.0	...	-0.183211	-0.207047	-0.291206	-0.159880	-0.091972	-0.246732	-0.426007	-0.574207	-0.926918	-1.215790
4	Andorra	AND	Europe & Central Asia	High income	100.0	100.000000	100.000000	100.000000	100.0	100.0	...	0.497262	0.355275	0.174378	1.100603	1.772183	1.580147	1.757491	1.761891	1.702288	0.994607

	1990(E)	1991(E)	1992(E)	1993(E)	1994(E)	1995(E)	1996(E)	1997(E)	1998(E)	1999(E)	2000(E)	2001(E)	2002(E)	2003(E)	2004(E)	2005(E)	2006(E)	2007(E)	2008(E)	2009(E)	2010(E)	2011(E)	2012(E)	2013(E)	2014(E)	2015(E)	2016(E)	2017(E)	2018(E)	2019(E)	2020(E)	2021(E)	1960(EG)	1961(EG)	1962(EG)	1963(EG)	1964(EG)	1965(EG)	1966(EG)	1967(EG)	1968(EG)	1969(EG)	1970(EG)	1971(EG)	1972(EG)	1973(EG)	1974(EG)	1975(EG)	1976(EG)	1977(EG)	1978(EG)	1979(EG)	1980(EG)	1981(EG)	1982(EG)	1983(EG)	1984(EG)	1985(EG)	1986(EG)	1987(EG)	1988(EG)	1989(EG)	1990(EG)	1991(EG)	1992(EG)	1993(EG)	1994(EG)	1995(EG)	1996(EG)	1997(EG)	1998(EG)	1999(EG)	2000(EG)	2001(EG)	2002(EG)	2003(EG)	2004(EG)	2005(EG)	2006(EG)	2007(EG)	2008(EG)	2009(EG)	2010(EG)	2011(EG)	2012(EG)	2013(EG)	2014(EG)	2015(EG)	1990(CO2)	1991(CO2)	1992(CO2)	1993(CO2)	1994(CO2)	1995(CO2)	1996(CO2)	1997(CO2)	1998(CO2)	1999(CO2)	2000(CO2)	2001(CO2)	2002(CO2)	2003(CO2)	2004(CO2)	2005(CO2)	2006(CO2)	2007(CO2)	2008(CO2)	2009(CO2)	2010(CO2)	2011(CO2)	2012(CO2)	2013(CO2)	2014(CO2)	2015(CO2)	2016(CO2)	2017(CO2)	2018(CO2)	2019(CO2)	2020(CO2)	1961(GDP annual growth)	1962(GDP annual growth)	1963(GDP annual growth)	1964(GDP annual growth)	1965(GDP annual growth)	1966(GDP annual growth)	1967(GDP annual growth)	1968(GDP annual growth)	1969(GDP annual growth)	1970(GDP annual growth)	1971(GDP annual growth)	1972(GDP annual growth)	1973(GDP annual growth)	1974(GDP annual growth)	1975(GDP annual growth)	1976(GDP annual growth)	1977(GDP annual growth)	1978(GDP annual growth)	1979(GDP annual growth)	1980(GDP annual growth)	1981(GDP annual growth)	1982(GDP annual growth)	1983(GDP annual growth)	1984(GDP annual growth)	1985(GDP annual growth)	1986(GDP annual growth)	1987(GDP annual growth)	1988(GDP annual growth)	1989(GDP annual growth)	1990(GDP annual growth)	1991(GDP annual growth)	1992(GDP annual growth)	1993(GDP annual growth)	1994(GDP annual growth)	1995(GDP annual growth)	1996(GDP annual growth)	1997(GDP annual growth)	1998(GDP annual growth)	1999(GDP annual growth)	2000(GDP annual growth)	2001(GDP annual growth)	2002(GDP annual growth)	2003(GDP annual growth)	2004(GDP annual growth)	2005(GDP annual growth)	2006(GDP annual growth)	2007(GDP annual growth)	2008(GDP annual growth)	2009(GDP annual growth)	2010(GDP annual growth)	2011(GDP annual growth)	2012(GDP annual growth)	2013(GDP annual growth)	2014(GDP annual growth)	2015(GDP annual growth)	2016(GDP annual growth)	2017(GDP annual growth)	2018(GDP annual growth)	2019(GDP annual growth)	2020(GDP annual growth)	2021(GDP annual growth)	2022(GDP annual growth)	1961(GDP per capita annual growth)	1962(GDP per capita annual growth)	1963(GDP per capita annual growth)	1964(GDP per capita annual growth)	1965(GDP per capita annual growth)	1966(GDP per capita annual growth)	1967(GDP per capita annual growth)	1968(GDP per capita annual growth)	1969(GDP per capita annual growth)	1970(GDP per capita annual growth)	1971(GDP per capita annual growth)	1972(GDP per capita annual growth)	1973(GDP per capita annual growth)	1974(GDP per capita annual growth)	1975(GDP per capita annual growth)	1976(GDP per capita annual growth)	1977(GDP per capita annual growth)	1978(GDP per capita annual growth)	1979(GDP per capita annual growth)	1980(GDP per capita annual growth)	1981(GDP per capita annual growth)	1982(GDP per capita annual growth)	1983(GDP per capita annual growth)	1984(GDP per capita annual growth)	1985(GDP per capita annual growth)	1986(GDP per capita annual growth)	1987(GDP per capita annual growth)	1988(GDP per capita annual growth)	1989(GDP per capita annual growth)	1990(GDP per capita annual growth)	1991(GDP per capita annual growth)	1992(GDP per capita annual growth)	1993(GDP per capita annual growth)	1994(GDP per capita annual growth)	1995(GDP per capita annual growth)	1996(GDP per capita annual growth)	1997(GDP per capita annual growth)	1998(GDP per capita annual growth)	1999(GDP per capita annual growth)	2000(GDP per capita annual growth)	2001(GDP per capita annual growth)	2002(GDP per capita annual growth)	2003(GDP per capita annual growth)	2004(GDP per capita annual growth)	2005(GDP per capita annual growth)	2006(GDP per capita annual growth)	2007(GDP per capita annual growth)	2008(GDP per capita annual growth)	2009(GDP per capita annual growth)	2010(GDP per capita annual growth)	2011(GDP per capita annual growth)	2012(GDP per capita annual growth)	2013(GDP per capita annual growth)	2014(GDP per capita annual growth)	2015(GDP per capita annual growth)	2016(GDP per capita annual growth)	2017(GDP per capita annual growth)	2018(GDP per capita annual growth)	2019(GDP per capita annual growth)	2020(GDP per capita annual growth)	2021(GDP per capita annual growth)	2022(GDP per capita annual growth)	1961(Population annual growth)	1962(Population annual growth)	1963(Population annual growth)	1964(Population annual growth)	1965(Population annual growth)	1966(Population annual growth)	1967(Population annual growth)	1968(Population annual growth)	1969(Population annual growth)	1970(Population annual growth)	1971(Population annual growth)	1972(Population annual growth)	1973(Population annual growth)	1974(Population annual growth)	1975(Population annual growth)	1976(Population annual growth)	1977(Population annual growth)	1978(Population annual growth)	1979(Population annual growth)	1980(Population annual growth)	1981(Population annual growth)	1982(Population annual growth)	1983(Population annual growth)	1984(Population annual growth)	1985(Population annual growth)	1986(Population annual growth)	1987(Population annual growth)	1988(Population annual growth)	1989(Population annual growth)	1990(Population annual growth)	1991(Population annual growth)	1992(Population annual growth)	1993(Population annual growth)	1994(Population annual growth)	1995(Population annual growth)	1996(Population annual growth)	1997(Population annual growth)	1998(Population annual growth)	1999(Population annual growth)	2000(Population annual growth)	2001(Population annual growth)	2002(Population annual growth)	2003(Population annual growth)	2004(Population annual growth)	2005(Population annual growth)	2006(Population annual growth)	2007(Population annual growth)	2008(Population annual growth)	2009(Population annual growth)	2010(Population annual growth)	2011(Population annual growth)	2012(Population annual growth)	2013(Population annual growth)	2014(Population annual growth)	2015(Population annual growth)	2016(Population annual growth)	2017(Population annual growth)	2018(Population annual growth)	2019(Population annual growth)	2020(Population annual growth)	2021(Population annual growth)	2022(Population annual growth)
count	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000	217.000000
mean	43.489329	46.004659	49.366703	51.154783	52.703799	54.736168	56.808000	58.373372	60.011029	62.150138	73.459199	73.967427	74.499641	75.075785	75.577143	76.019012	76.923533	77.292335	77.957729	78.430723	79.280472	79.985660	80.679052	81.235389	81.966951	82.521371	83.489038	84.218849	84.816159	85.363756	85.892042	86.373922	264.062811	268.187152	279.194845	294.338936	306.932011	323.790227	332.092316	340.680482	363.175533	384.993621	415.744950	983.534801	998.586195	1084.752793	1060.843580	1005.934178	1047.392524	1069.846330	1078.613745	1134.234334	1147.868755	1143.944787	1163.379980	1162.370219	1161.320689	1150.505869	1166.325275	1176.640371	1187.572960	1209.759383	1566.965904	1477.378494	1452.199732	1442.304909	1442.652198	1446.310513	1473.570203	1520.727395	1536.756875	1521.720659	1543.271417	1582.943954	1592.941173	1628.766533	1759.271044	1764.109100	1809.071969	1820.333609	1732.555355	1668.810725	1717.704338	1723.356285	1722.442084	1698.565014	1662.182533	655.161540	93763.963016	94412.362824	94544.351199	95179.136692	95719.473249	98425.445838	100517.519718	102283.502277	102835.554812	103348.794384	106586.336120	108390.675175	109829.542627	115001.552419	120130.946714	124657.803477	128696.428972	133560.507772	134564.578185	133035.786773	141209.216939	145663.353466	147631.793974	150633.028820	151086.416285	149964.836008	150105.072757	152336.727186	156245.920083	155930.990907	148943.605131	1.671458	2.313185	2.186088	2.671147	2.357393	2.194625	2.259751	3.049710	3.148447	3.508088	2.771929	2.991690	2.606149	2.980520	1.703452	3.443774	2.684339	2.860275	2.507445	1.868443	2.133314	1.003334	1.077603	2.224569	2.106418	2.624988	2.743790	3.445901	2.263204	2.539595	1.230054	1.246148	1.618922	1.773452	3.298974	4.241317	4.486338	3.192266	2.876547	3.788030	3.033952	2.920878	3.592008	5.431132	4.595557	5.236552	5.241805	3.692486	-0.200104	3.934932	3.368764	3.018480	2.820088	3.050597	2.499586	2.938269	3.023440	2.897285	2.702009	-4.996384	5.198718	3.830875	0.681620	1.244591	1.131709	1.606476	1.312223	1.107224	1.132206	1.920430	1.995911	2.354107	1.553891	1.806080	1.445676	1.789197	0.525972	2.185883	1.438262	1.604518	1.209651	0.568230	0.721811	-0.421335	-0.354279	0.813634	0.694131	1.188073	1.247861	1.960190	0.787534	1.047772	-0.291973	-0.188821	0.235795	0.457040	2.027233	2.758220	3.086517	1.904129	1.619351	2.524403	1.771119	1.630578	2.292065	4.091092	3.225777	3.752001	3.755220	2.237681	-1.581172	2.594070	2.162453	1.785126	1.452135	1.713475	1.226397	1.705699	1.843871	1.722501	1.569738	-5.915502	4.324804	2.894463	2.264454	2.330707	2.380858	2.316108	2.304132	2.258272	2.253934	2.277120	2.239347	2.216607	2.241673	2.174686	2.158036	2.139245	2.094872	2.099519	2.052053	1.981784	1.979243	2.037863	2.021840	2.087415	2.152887	2.114348	2.069623	2.052115	2.050109	1.963819	1.933231	1.699442	1.601467	1.732981	1.623664	1.500728	1.484108	1.668160	1.624936	1.521329	1.428115	1.406551	1.376862	1.421472	1.411257	1.413058	1.449045	1.545530	1.542833	1.517732	1.447629	1.355594	1.264662	1.322436	1.377639	1.345282	1.287322	1.229685	1.151059	1.138190	1.121918	1.008800	0.848505	0.878030
std	48.907388	48.315766	47.709248	46.885191	46.489787	45.846403	44.945891	44.609569	43.794481	43.014116	35.085949	34.850426	34.379763	33.818423	33.393834	33.104228	32.642630	32.029149	31.717618	31.306659	30.792498	29.998803	29.445194	29.183726	28.635353	28.115342	27.357172	26.767866	25.960180	25.744857	25.387192	24.905878	1017.679239	1022.648403	1041.560881	1077.213818	1127.745938	1147.256662	1153.432518	1170.355261	1246.461739	1320.940568	1395.471565	2932.233999	2895.053095	3227.942399	3029.714281	2396.686780	2540.925189	2539.995545	2408.904202	2523.249021	2585.256790	2545.302704	2679.737891	2725.267926	2437.883652	2233.385593	2246.897849	2229.904505	2248.533890	2305.430463	2294.768874	2327.168681	2303.400847	2301.261097	2329.112190	2320.956962	2360.679480	2575.407326	2655.723558	2564.256840	2565.563006	2667.870568	2703.126784	2716.643885	2801.387154	2737.396592	2782.036422	2813.705496	2816.081686	2738.272900	2785.021740	2873.683791	2866.302772	2815.857667	2823.662664	1879.400593	405593.962471	406328.762106	410280.953519	419132.887899	423505.745749	435796.933877	443974.682287	459022.725143	464325.040883	464160.142557	480652.754670	485351.058630	486842.827746	514800.828696	547221.078060	579875.902009	605453.063262	637727.395130	641295.333073	652697.712970	704684.966140	744834.482045	754886.210006	784670.534192	788926.877865	775937.949405	773434.615661	786323.727832	820818.216035	827715.061928	821874.441016	4.406033	4.345739	4.609193	4.524325	4.194551	4.081920	5.872687	6.699031	4.779952	6.387050	4.554912	5.477454	5.358358	6.081975	5.266602	6.016418	4.763407	5.248625	5.854376	5.675049	4.961015	4.510804	4.608077	4.094936	4.125180	4.425512	4.651521	5.397259	5.215615	6.515361	7.600735	8.069489	6.779419	7.250956	5.138925	8.256008	11.065553	5.099460	4.358260	4.049576	5.532003	4.494740	5.606206	5.998920	4.421342	4.708579	4.241795	6.051326	5.655943	4.457708	5.644571	8.325560	5.044485	3.694823	4.689391	3.738093	4.102022	3.127704	3.525398	8.429749	6.070269	6.427968	3.958586	3.544250	4.008325	3.760477	3.517736	3.446455	5.394319	6.090062	4.098207	5.518800	3.841689	4.842087	4.797148	5.466268	4.915792	5.278440	4.234676	4.968219	5.413943	5.198012	4.585359	4.358066	4.462551	3.966861	4.068728	4.331611	4.534027	5.184153	5.108612	6.179083	7.263615	7.640094	6.339078	6.764802	5.976519	7.686015	10.423881	4.963977	4.177112	4.042049	5.313032	4.396537	5.468723	5.555608	4.215258	4.423173	4.272250	5.943267	5.123298	4.107765	5.375299	8.569161	4.674218	3.422074	4.693460	3.790104	4.138828	3.020802	3.473157	8.273661	6.258276	6.279185	1.468415	1.502761	1.663652	1.501529	1.548015	1.551840	1.586755	1.854218	1.980709	1.861918	1.901313	1.777686	1.737723	1.578829	1.617631	1.903800	1.913568	1.846858	1.894073	1.645424	1.847492	1.942834	2.045677	1.934482	1.821101	1.766101	1.719653	1.676138	1.683553	2.729484	2.504301	2.171485	1.881062	1.982094	1.906994	1.752106	1.749015	1.647788	1.672460	1.370743	1.392488	1.378254	1.392322	1.359771	1.513138	2.018527	2.251149	2.054745	1.763262	1.515568	1.616468	1.529739	1.618706	1.709519	1.496230	1.363899	1.319466	1.332566	1.262967	1.235314	1.342774	1.648741
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-27.270000	-19.685042	-12.278657	-12.464993	-12.481834	-7.659066	-15.743628	-5.474906	-6.554143	-5.649650	-11.331719	-13.973729	-17.047584	-18.266379	-14.803126	-26.768233	-12.582091	-24.049206	-26.478789	-21.599649	-19.826716	-20.729888	-16.051386	-16.819835	-11.144353	-14.958137	-17.146042	-13.379844	-42.451118	-14.788226	-64.047107	-44.899999	-29.300002	-50.248067	-12.416322	-16.699998	-14.115381	-28.099980	-9.424162	-14.277000	-9.310638	-12.489192	-36.658153	-5.807538	-12.702893	-8.510638	-6.976744	-17.668946	-17.573222	-8.924176	-50.338515	-46.082122	-36.391977	-23.042806	-27.994546	-9.375124	-8.000000	-19.268850	-11.320755	-54.335876	-20.738839	-29.100000	-26.527644	-20.910074	-14.521498	-14.404949	-15.115017	-10.550791	-17.500310	-6.758929	-9.268812	-9.012213	-13.307118	-15.177659	-19.229269	-20.316483	-16.539297	-26.325029	-14.628880	-25.884051	-28.440545	-24.277368	-22.378561	-24.430735	-19.945535	-19.291714	-13.852511	-20.120820	-19.405123	-15.246304	-43.566902	-14.765157	-64.425841	-45.325107	-29.841290	-41.586861	-14.077047	-18.348264	-13.590923	-29.413291	-11.202914	-16.419985	-11.610002	-14.964033	-38.561720	-6.466319	-12.635127	-8.297335	-13.923387	-18.323503	-17.145394	-13.154428	-47.899986	-48.392454	-36.777697	-24.463161	-29.921761	-12.202835	-9.033697	-18.586782	-12.429867	-55.188681	-22.966942	-22.419285	-1.015528	-1.510091	-1.845309	-2.110700	-2.354033	-2.596081	-2.829547	-3.085539	-4.787105	-2.562076	-1.591048	-2.135137	-2.423670	-2.545805	-2.719639	-6.458686	-4.324284	-8.262824	-9.249471	-3.924172	-11.275324	-10.053006	-1.365708	-0.517770	-0.558423	-0.608561	-1.210190	-1.626002	-2.273961	-27.722225	-22.347974	-5.964186	-8.101781	-16.049153	-16.880630	-3.629546	-3.207518	-5.924733	-10.955149	-4.075386	-3.847671	-2.958573	-3.345834	-3.720032	-4.077969	-4.390534	-4.663536	-4.905031	-5.084451	-5.343938	-4.793554	-5.280078	-5.033810	-6.852118	-4.415744	-2.217280	-3.755484	-4.048391	-2.904996	-2.984077	-4.256649	-14.257037
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.826888	7.325682	43.700000	46.200000	47.000000	51.516457	51.871391	55.227303	55.800335	56.910419	59.122952	59.568451	63.167737	64.062560	65.746750	69.413651	71.201126	72.984283	75.920000	79.200000	80.700829	83.500000	85.635345	86.291473	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	128.303790	137.847375	139.061470	153.276840	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	194.500000	221.400000	282.610000	252.100000	278.300000	292.900000	310.700000	338.300000	377.420000	378.530000	388.840000	442.500000	443.760000	450.200000	416.900000	446.500000	518.700000	491.600000	474.300000	510.600000	519.100000	573.400000	525.600000	558.790000	506.800000	585.900000	593.200000	602.300000	656.100000	697.200000	687.600000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.185104	1.014396	0.068038	0.000000	1.127907	0.213333	0.257180	1.037983	2.570134	2.228270	2.646955	2.620331	0.446929	-3.764578	1.395939	1.107544	0.000000	0.632137	0.984469	0.967703	1.164724	1.317188	1.393176	0.818526	-7.816951	2.111831	1.700000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-0.061308	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-1.740252	-1.057654	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-0.798462	-1.834742	-1.736630	-1.319461	-0.007442	0.000000	0.000000	0.000000	0.000000	-0.109438	0.000000	0.000000	0.000000	0.000000	1.197055	0.788917	1.103829	0.975922	0.000000	-4.611748	0.121592	0.025024	-0.551991	0.000000	0.174328	-0.024726	0.010796	0.077665	0.182368	0.000000	-8.672432	1.013321	0.000000	1.402060	1.366899	1.378477	1.382205	1.359458	1.241618	1.223654	1.226726	1.110437	1.191297	1.205085	1.122448	1.066259	1.067901	1.063726	1.002658	0.963867	0.850876	0.809721	0.862384	0.776216	0.894085	0.894335	0.856055	0.811017	0.732589	0.810550	0.648304	0.765915	0.712313	0.681643	0.650644	0.588262	0.447447	0.492793	0.535571	0.514767	0.574789	0.516384	0.565925	0.468037	0.458574	0.433075	0.432788	0.463308	0.476921	0.455526	0.512387	0.478246	0.425941	0.355338	0.376272	0.416901	0.359828	0.354975	0.287421	0.234671	0.323321	0.290665	0.143641	0.105799	0.185163
50%	0.000000	5.600000	49.200000	61.384029	69.253830	74.915398	77.492043	82.029404	85.040000	88.518177	95.416351	96.276352	96.100000	96.297951	96.615067	96.725266	97.773399	97.224396	97.942963	98.296799	98.691376	98.840000	99.100000	99.145050	99.274551	99.409804	99.654099	99.851913	99.820000	99.900000	99.990000	100.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	188.465042	197.934041	213.718746	226.062858	252.530536	259.179567	262.447866	267.046341	273.907471	273.651797	268.733676	272.461128	276.230740	277.953645	290.501090	279.205331	281.783690	278.999338	276.995339	531.569298	487.880042	468.394735	465.482265	465.158384	474.463701	474.417013	491.812232	486.732273	486.344819	505.936912	480.887218	500.298345	511.252355	683.277637	722.945627	729.808628	741.280222	574.121208	583.805983	625.268921	608.866553	656.711173	631.919735	554.226022	0.000000	3179.000000	3193.420000	3585.400000	3795.200000	3252.100000	3781.200000	4053.600000	4493.900000	4767.340000	4745.200000	4772.800000	4708.800000	4620.100000	5206.000000	5459.900000	5609.900000	5584.400000	6131.500000	6474.700000	6666.200000	6539.200000	7430.800000	7299.500000	7402.300000	7282.300000	7537.900000	8143.000000	8173.500000	8589.000000	8965.700000	8312.500000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.041547	0.000000	0.000000	0.000000	0.000000	1.287558	1.420933	1.867612	1.682282	2.655262	2.150267	0.999993	1.015128	0.929214	1.233558	2.400691	3.030863	3.667180	3.966651	3.477834	2.902214	3.868718	2.626010	3.014283	3.747398	4.784467	4.310239	5.244100	5.168231	3.436374	0.000000	3.731140	3.421809	2.690350	3.004823	3.090328	2.767676	2.953509	3.079851	2.918307	2.500000	-3.739562	4.687478	3.674517	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.410835	0.410783	0.000000	0.000000	0.000000	0.000000	0.768829	1.758333	1.994095	2.100125	1.781324	1.339021	2.689647	1.286508	1.313732	2.514866	3.505743	2.877489	3.501312	3.621787	1.838240	-1.256816	2.485985	2.402779	1.348536	1.538974	1.765941	1.622166	1.812607	1.860147	1.876401	1.568211	-4.792468	3.752521	2.386469	2.213284	2.311689	2.315196	2.381616	2.382829	2.311738	2.302016	2.268876	2.263434	2.234855	2.200844	2.157027	2.196357	2.198373	2.172281	2.091391	2.032555	1.964645	1.957311	2.047964	2.002974	2.075682	2.092556	2.120070	2.075109	2.059502	2.022787	2.016939	1.965285	1.896626	1.900942	1.775504	1.671828	1.569307	1.589701	1.660946	1.578534	1.542806	1.431673	1.383747	1.343320	1.277639	1.254593	1.269573	1.319053	1.294402	1.233193	1.270618	1.249485	1.251517	1.245454	1.243571	1.159251	1.132158	1.120928	1.121608	1.088676	1.140549	1.074975	0.970386	0.844182	0.884624
75%	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	99.982925	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	799.749504	865.592481	841.332828	823.743652	826.952081	876.299690	867.229471	912.906025	923.227200	988.003660	934.157558	1040.545443	972.687917	1051.353989	1029.125832	1121.922084	1101.294740	1121.570432	1235.014149	2280.882812	2201.633412	2110.495548	2049.448756	1947.807360	1879.094587	1986.836971	2057.432496	2077.574238	2081.749959	2039.054164	2109.680384	2119.782080	2223.783121	2252.855716	2261.483778	2314.441463	2402.426395	2337.271387	2295.343190	2222.629692	2211.807053	2149.602569	2204.243299	2176.843306	0.000000	40868.900000	37788.500000	36857.300000	40504.600000	38651.800000	42370.600000	44006.000000	42273.500000	44630.800000	44322.000000	44401.100000	46185.600000	45596.100000	48931.500000	49365.200000	50592.500000	52930.400000	52598.700000	53014.200000	48765.700000	50937.300000	51230.200000	48220.300000	49751.900000	46161.700000	49851.900000	48694.800000	53272.700000	50121.300000	54692.700000	46324.300000	3.844734	4.482859	4.405974	5.459057	4.760000	4.441159	4.313622	5.093751	5.574674	5.598515	4.698992	5.643553	5.896580	5.533804	2.923532	6.080396	5.100590	6.208864	4.939124	4.564243	4.814820	2.634297	3.491525	4.792699	4.241336	4.776564	4.764533	5.996855	4.777585	4.458587	4.291342	4.758581	4.850001	5.100000	5.911908	6.047268	5.925837	4.993528	4.699227	5.798782	4.805453	4.848013	6.353137	7.032395	6.699999	7.414121	7.415952	6.225894	3.368975	6.402565	6.039008	5.456389	5.227704	5.066422	4.489282	4.750317	4.716465	4.774417	4.665678	-0.819785	7.403368	5.648325	1.496392	2.021470	2.557472	3.291864	2.490604	2.240466	2.124083	3.530467	3.405688	3.180126	2.684019	3.612610	3.979082	3.172396	1.186000	4.446607	3.233480	4.198675	3.162182	2.451575	2.680776	1.141571	1.898275	3.019292	2.682174	2.705521	2.978870	4.188665	3.259730	3.095333	2.285325	3.138686	3.150491	3.550976	3.819876	4.461776	4.372417	4.068483	3.688198	4.293374	3.214135	3.545381	4.642978	5.524827	5.180369	5.886420	6.067421	4.513711	1.392178	4.601538	4.735771	3.780489	3.487417	3.653587	3.508054	3.383651	3.745446	3.624263	3.354972	-1.953084	6.500471	4.831341	2.958751	2.968333	2.963649	2.945226	2.955650	2.923062	2.917223	2.912352	2.858207	2.824324	2.789148	2.731882	2.781958	2.823886	2.906562	2.878888	2.845966	2.858475	2.831005	2.805115	2.816197	2.823571	2.801671	2.833689	2.853199	2.911396	2.921521	2.869367	2.797643	2.816912	2.695093	2.699827	2.596347	2.520597	2.501899	2.465731	2.351452	2.330204	2.388501	2.404784	2.402401	2.414982	2.310405	2.372008	2.424600	2.359211	2.293356	2.299710	2.342411	2.351856	2.207643	2.179882	2.238167	2.194277	2.200322	2.155312	2.079223	2.020537	1.950856	1.897015	1.796101	1.893246
max	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	10523.406690	10534.018210	10414.540920	10465.813020	11150.050340	10926.395170	10480.606180	10361.972570	11114.667260	11974.192590	12106.152940	37113.339530	36146.699610	40710.112180	37557.340360	25763.473230	28353.128200	28090.673700	25027.221060	25303.041580	26517.451250	25001.641670	27354.021630	28902.850640	19859.938130	15434.159310	15688.816930	15567.239820	15695.517560	15525.980570	14776.657040	15820.493950	16022.739570	16120.991850	15708.122700	15819.141850	16337.335740	18479.284990	18814.372340	18454.004720	16906.150290	17777.159560	19140.337860	18879.894460	21420.628500	19635.424350	19300.107380	17969.106890	16353.831270	16911.079780	17023.166820	18157.598100	19987.584350	19819.982890	19903.475110	17478.893040	4844517.400000	4807496.500000	4879626.100000	4995209.500000	5066803.000000	5117036.900000	5273486.300000	5543349.400000	5590536.300000	5609017.300000	5775807.200000	5748261.800000	5593024.400000	5658992.000000	5738286.000000	5824625.100000	6437470.300000	6993182.800000	7199604.700000	7719071.400000	8474922.700000	9282553.700000	9540539.700000	9979128.000000	10021043.400000	9859281.200000	9860914.000000	10089273.200000	10567262.000000	10762824.000000	10944686.200000	14.766843	24.521297	34.313729	27.690886	17.035712	19.200000	66.219084	81.887797	25.666169	58.647331	25.821107	27.423969	26.404881	45.302754	30.073699	35.625338	23.307491	22.003005	26.139296	24.753727	19.688334	23.597700	16.666671	16.711594	21.792774	24.542499	29.054324	34.600001	14.190637	57.817828	49.447379	34.745320	33.990468	21.221411	35.224078	88.957666	149.972963	34.857095	25.664015	19.681269	63.379875	26.524135	17.326020	53.381794	27.961538	34.500000	25.000000	65.000000	21.390528	25.122595	21.616648	86.826748	21.079014	15.217391	24.475253	29.212121	32.491802	8.668006	23.412017	43.479652	37.687192	63.368439	13.443330	20.871865	32.168302	24.077314	14.197889	15.226092	62.343394	77.511102	22.548707	51.857146	23.343793	24.516490	22.131876	44.456487	26.921565	32.563746	20.100419	20.442132	23.604162	20.920310	19.208867	20.940746	11.717021	13.690020	20.639619	19.733137	25.319721	32.009167	11.671573	55.889245	46.470687	29.855792	31.315593	17.596283	60.090541	81.355165	140.480042	30.629377	20.811297	18.911492	55.590330	19.557657	14.773836	49.031637	26.660095	33.030488	23.590685	65.386605	17.143534	21.917391	18.697438	96.956420	18.014653	12.626580	23.304693	30.174901	30.496301	8.411602	21.619974	43.758799	35.833873	62.528291	10.638254	11.774148	12.851885	12.147917	11.964503	11.988676	12.114861	12.612111	17.039974	16.295475	14.475650	13.006835	11.808203	10.810504	9.944741	13.168342	14.055088	13.029515	12.406977	10.996347	12.218296	12.920716	20.473239	19.052111	16.686319	14.873595	13.420984	12.171292	11.047226	10.219241	8.522739	19.052040	14.964455	9.864262	6.046536	16.625502	13.247067	11.897899	10.699857	5.580387	5.785413	6.449321	7.541019	6.851939	8.706429	17.898545	19.360429	17.399086	13.422921	6.611552	10.684073	9.758169	9.226496	11.794016	9.219918	7.212802	4.394554	4.556082	3.931356	3.727101	3.707424	3.712988

	Country Name	Country Code	Region	IncomeGroup	1990(E)	1991(E)	1992(E)	1993(E)	1994(E)	1995(E)	...	2015(Population annual growth)	2016(Population annual growth)	2017(Population annual growth)	2018(Population annual growth)	2019(Population annual growth)	2020(Population annual growth)	2021(Population annual growth)	2022(Population annual growth)	latest_gdp_per_capita	latest_co2
0	American Samoa	ASM	East Asia & Pacific	High income	0.000000	0.000000	0.000000	0.000000	0.00000	0.0	...	-1.639270	-1.807231	-1.971819	-2.122936	-2.304139	-2.421250	-2.530171	-1.706495	3.486	0.0
1	Guam	GUM	East Asia & Pacific	High income	99.844307	99.875816	99.906868	99.937256	99.96508	100.0	...	0.259298	0.218837	0.154325	0.042694	-0.032019	0.359326	0.767004	0.724497	0.000	0.0
2	Timor-Leste	TLS	East Asia & Pacific	Lower middle income	0.000000	0.000000	0.000000	0.000000	0.00000	0.0	...	1.755472	1.542920	1.513362	1.485808	1.462727	1.515821	1.598470	1.529119	-21.747	446.1

	Country Name	Country Code	Region	IncomeGroup	1990(E)	1991(E)	1992(E)	1993(E)	1994(E)	1995(E)	...	2015(Population annual growth)	2016(Population annual growth)	2017(Population annual growth)	2018(Population annual growth)	2019(Population annual growth)	2020(Population annual growth)	2021(Population annual growth)	2022(Population annual growth)	latest_gdp_per_capita	latest_co2
0	Aruba	ABW	Latin America & Caribbean	High income	100.0	99.153656	99.197128	99.239914	100.000000	100.000000	...	0.637959	0.590062	0.537296	0.494795	0.451970	0.134255	-0.045045	-0.086392	10.554	0.00
1	Afghanistan	AFG	South Asia	Low income	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	3.121341	2.581549	2.866492	2.885208	2.908529	3.134747	2.851358	2.534498	0.000	8709.47
2	Angola	AGO	Sub-Saharan Africa	Lower middle income	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	3.617678	3.586211	3.550987	3.464457	3.395278	3.268348	3.166030	3.096753	-0.097	19814.50
3	Albania	ALB	Europe & Central Asia	Upper middle income	100.0	100.000000	100.000000	100.000000	100.000000	100.000000	...	-0.291206	-0.159880	-0.091972	-0.246732	-0.426007	-0.574207	-0.926918	-1.215790	6.139	4383.20
4	Andorra	AND	Europe & Central Asia	High income	100.0	100.000000	100.000000	100.000000	100.000000	100.000000	...	0.174378	1.100603	1.772183	1.580147	1.757491	1.761891	1.702288	0.994607	7.733	448.88
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
212	Kosovo	XKX	Europe & Central Asia	Upper middle income	100.0	100.000000	100.000000	100.000000	100.000000	100.000000	...	-1.364932	-0.596734	0.753585	0.339011	-0.457730	0.070131	-0.229016	-1.355874	6.655	0.00
213	Yemen, Rep.	YEM	Middle East & North Africa	Low income	0.0	0.000000	44.100000	40.774715	41.985134	43.193512	...	2.712955	2.621537	2.564321	2.486360	2.426208	2.310447	2.137790	2.144628	0.000	9960.10
214	South Africa	ZAF	Sub-Saharan Africa	Upper middle income	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	...	2.074017	0.972004	0.387278	1.225530	1.295074	1.223179	0.998920	0.841058	1.057	393241.60
215	Zambia	ZMB	Sub-Saharan Africa	Lower middle income	13.9	12.752703	19.200000	14.213207	14.941759	15.668272	...	3.191896	3.147407	3.113595	3.061888	3.007618	2.933818	2.840806	2.758032	2.386	7607.10
216	Zimbabwe	ZWE	Sub-Saharan Africa	Lower middle income	0.0	0.000000	28.200000	31.163668	28.100000	31.948580	...	2.136294	2.081806	2.043620	2.020537	1.989253	2.031112	2.045715	2.024036	4.388	8312.50

Model	MSE	MAE	RMSE	R2
Ridge Regression	1.558e10	81629.23	124805.21	0.4345
Linear Regression	1.532e10	80348.83	123771.48	0.4438
Decision Tree (Tuned)	1.865e10	27348.99	136555.57	0.3230
Random Forest (Tuned)	9.747e9	26766.68	98726.23	0.6461
Gradient Boosting (Tuned)	1.148e10	26766.68	98726.23	0.5834
Neural Network (Tuned)	1.594e10	90547.34	126263.86	0.4212

Full Name: Abdullah Al Rakin¶

Course Name: CSC761_UT1 - Advanced Artificial Intelligence¶

Project Name: Sustainable Future Forecasting: Socioeconomic & Environmental Analysis for Emissions Reduction¶

Import all necessary libraries¶

Read the dataset into a dataframe 'df'¶

Data Preprocessing¶

Display the dataframe¶

Print out all the features of df¶

Check the dimensions of the dataset¶

Check the data types of each column¶

Check for missing values¶

Summary statistics for numerical variables¶

Extracting relevant columns related to CO2 emissions & GDP per capita from the DataFrame¶

Display the extracted columns¶

Exploratory Data Analysis (EDA)¶

Visualization 01: CO2 emissions over time using line plot¶

Visualization 02: Top 10 Countries by GDP per Capita in 2022 using Bar Plot¶

Visualization 03: Top 10 Countries by CO2 Emissions in 2020¶

Visualization 04: CO2 Emissions vs GDP per Capita Annual Growth in 2022 using Scatter Plot¶

Visualization 05: CO2 Emissions by Region (2020) using Stacked Bar Chart¶

Visualization 06: Area Plot of the Countries¶

Visualization 07: Scatter Plot Matrix of Selected Features (2015)¶

Visualization 08: 3D Plot visualization of the dataset¶

Visualization 09: STL Decomposition of CO2 over the years¶

Visualization 10: CO2 Emissions Over the Years using scatterplot¶

Visualization 11: GDP per Capita Over Time¶

Visualization 12: CO2 Emissions Worldwide using Choropleth¶

Visualization 13: PCA Cluster Analysis & t-SNE Cluster Analysis¶

Visualization 14: Correlation Matrix of All the Numerical Features¶

Checking for discrepancies between DataFrame columns & correlation matrix columns/index¶

Outlier Detection¶

Visualization 15: Boxplot visualization 'Electricity Usage' in 2015¶

Visualization 16: Highlighted Outliers of 'Electricity' in 2015¶

Visualization 17: Boxplot visualization Energy Usage in 2015¶

Visualization 18: Highlighted Outliers of Energy Usage in 2015¶

Visualization 19: Boxplot visualization CO2 Emission in 2015¶

Visualization 20: Highlighted Outliers of CO2 Emission in 2015¶

Visualization 21: Boxplot visualization GDP Annual, GDP Per Capita & Population Growth in 2015¶

Visualization 22: Highlighted Outliers of GDP Annual Growth in 2015¶

Visualization 23: Highlighted Outliers of GDP Per Capita Annual Growth in 2015¶

Visualization 24: Highlighted Outliers of Population Annual Growth in 2015¶

Outlier Removal Process¶

As the filtered dataset is significantly reduced in size due to the presence of numerous zero values, I decided to utilize the entire dataset for the project to ensure a more comprehensive analysis and modeling approach.¶

Model Training¶

Define features and target variable¶

Split features (X) and target variable (y)¶

One-hot encode categorical variables¶

Splitting the dataset into the Training set and Test set¶

Standardizing features by removing the mean and scaling to unit variance¶

Ridge Regression¶

Visualization 25: Prediction vs. Actual¶

Visualization 26: Residual Plot¶

Visualization 27: Distribution of Residuals¶

Linear Regression¶

Visualization 28: Prediction vs. Actual¶

Visualization 29: Residual Plot¶

Visualization 30: Feature Importance Plot¶

Decision Trees¶

Visualization 31: Prediction vs. Actual¶

Visualization 32: Decision Tree Visualization¶

Visualization 33: Feature Importance Plot¶

Random Forest¶

Visualization 34: Decision Tree Visualization¶

Visualization 35: Mean Squared Error (MSE) vs Number of Trees¶

Visualization 36: Feature Importance¶

Gradient Boosting¶

Visualization 37: Prediction vs. Actual Plot¶

Visualization 38: Residual Plot¶

Visualization 39: Learning Curve Visualization¶

Visualization 40: Learning Rate Curve¶

Neural Network¶

Visualization 41: Prediction vs. Actual Plot¶

Visualization 42: Loss Curve (Training vs. Validation)¶

Visualization 43: Learning Curve¶

Comparison of Results of Each Models¶

Project Overview¶

Table of Contents¶

Introduction ¶

Data Preprocessing ¶

Overview¶